fde.today

Welcome to fde.today! Drop a comment below and tell us: Your name (or handle) Your current role - FDE, aspiring FDE, hiring manager, or just curious? Your background - CS degree? Bootcamp? Self-taught? What brought you here? One hot take about Forward Deployed Engineering Looking forward to meeting everyone!

The Definitive FDE Company Directory This is the most comprehensive, actively maintained list of companies hiring Forward Deployed Engineers, Forward Deployed Software Engineers (FDSEs), and equivalent roles. Updated monthly. Tier 1: Established FDE Programs (50+ FDEs) Palantir Technologies Roles: FDSE, Forward Deployed Infrastructure Engineer, Deployment Strategist Comp range: $170K-$500K+ total comp (L3-L6) Locations: NYC, Palo Alto, DC, Denver, London, globally Work style: Hybrid, 30-50% travel to client sites Tech stack: Java, Python, Foundry/Gotham/Apollo, Spark Security clearance: Required for government work (TS/SCI for some roles) Notable: Invented the FDE role. Largest FDE org in the world. Known for intense decomposition interviews. Databricks Roles: AI FDE, Forward Deployment Engineer, Resident Solutions Architect Comp range: $200K-$520K+ total comp Locations: SF, NYC, remote eligible, global Work style: Hybrid, 20-30% travel Tech stack: Python, Spark, SQL, Delta Lake, MLflow, Unity Catalog Notable: Rapidly expanding FDE org. Created "Head of AI FDE" role in 2025. Strong equity component. Snowflake Roles: Forward Deployed Engineer, Solutions Engineer Comp range: $180K-$350K total comp Locations: San Mateo, remote eligible Work style: Hybrid, 25-35% travel Tech stack: SQL, Python, Snowpark, Streamlit Tier 2: Growing FDE Programs (10-50 FDEs) Scale AI Roles: Forward Deployed AI Engineer Comp range: $190K-$400K+ total comp Locations: SF, NYC Work style: Hybrid, 15-25% travel Tech stack: Python, LLMs, data annotation pipelines, ML evaluation Notable: FDEs deploy AI solutions directly with enterprise customers. Fast-growing team. Anduril Industries Roles: Forward Deployed Engineer, Mission Software Engineer Comp range: $180K-$380K total comp Locations: Costa Mesa, DC, Seattle Work style: On-site, 10-20% travel Tech stack: C++, Python, Rust, computer vision, autonomy systems Security clearance: Required (most roles) Notable: Defense-focused FDE work. Hardware + software integration. Anthropic Roles: Forward Deployed Engineer, Solutions Engineer Comp range: $250K-$600K+ total comp Locations: SF, NYC Work style: Hybrid Tech stack: Python, Claude API, RAG, agent frameworks Notable: FDEs help enterprise customers deploy Claude. Top-of-market comp. OpenAI Roles: Solutions Engineer, Forward Deployed Engineer Comp range: $280K-$700K+ total comp Locations: SF Work style: On-site preferred Tech stack: Python, GPT APIs, fine-tuning, agent systems Notable: Highest comp in the FDE space. Small, selective team. Stripe Roles: Forward Deployed Engineer, Solutions Architect Comp range: $200K-$400K total comp Locations: SF, NYC, Seattle, remote Work style: Remote-friendly, 15% travel Tech stack: Ruby, Python, APIs, payments infrastructure Datadog Roles: Forward Deployed Engineer, Solutions Engineer Comp range: $180K-$340K total comp Locations: NYC, Boston, remote Work style: Hybrid, 25% travel Tech stack: Python, Go, observability, cloud infrastructure Tier 3: Emerging FDE Programs Cohere Comp range: $150K-$280K total comp Locations: Toronto, SF, remote Tech stack: Python, LLMs, RAG, embeddings ElevenLabs Comp range: $160K-$300K total comp Locations: NYC, SF, London, remote Tech stack: Python, audio ML, APIs MongoDB Comp range: $170K-$320K total comp Locations: NYC, Austin, remote Tech stack: MongoDB, Python, Node.js, Atlas Confluent Comp range: $160K-$280K total comp Locations: Remote, Mountain View Tech stack: Kafka, Java, Python, stream processing HashiCorp Comp range: $180K-$350K total comp Locations: SF, remote Tech stack: Go, Terraform, Vault, Consul dbt Labs Comp range: $155K-$270K total comp Locations: Remote Tech stack: SQL, Python, dbt, analytics engineering Salesforce (Agentforce) Roles: AI Forward Deployed Engineer (Agentforce) Comp range: $200K-$380K total comp Locations: SF, NYC, remote Tech stack: Python, LLMs, Salesforce platform, agent frameworks Notable: New program specifically for deploying AI agents to enterprise customers. Ramp Comp range: $180K-$350K total comp Locations: NYC Tech stack: Python, TypeScript, fintech integrations Tier 4: Companies with FDE-Adjacent Roles These companies have roles that function like FDEs but may use different titles: Company Title Comp Range Google Cloud Customer Engineer $180K-$400K AWS Solutions Architect (ProServ) $170K-$350K Microsoft Cloud Solution Architect $160K-$330K Elastic Solutions Architect $160K-$300K Twilio Solutions Engineer $150K-$280K Vercel Solutions Engineer $160K-$300K FDE Hiring Trends (2025-2026) 800% growth in FDE job postings since early 2025 58% of FDE roles are at companies with 11-200 employees AI FDE is the fastest-growing sub-role Security clearance commands $30K-$80K salary premium Remote FDE roles growing, but hybrid still dominates (60%+) This directory is updated monthly. Have a company to add? Reply below or submit a suggestion. Salary data sourced from our FDE Salary Database and community submissions.

FDE Job Board This thread is for posting Forward Deployed Engineer job opportunities. How to Post Include the following: Company name Role title Location (remote/hybrid/on-site + city) Compensation range (if possible) Brief description of the FDE work Link to apply How to Find FDE Jobs Search these terms on job boards: "Forward Deployed Engineer" "Forward Deployed Software Engineer" "Field Engineer" + (software or data) "Solutions Engineer" (many are FDE-like) "Customer Engineer" "Implementation Engineer" "Technical Account Manager" (at technical companies) Best Job Boards for FDE Roles LinkedIn (search the terms above) Glassdoor levels.fyi (for compensation data) Palantir, Databricks, Scale AI careers pages directly Y Combinator Work at a Startup (many startups need FDEs) Wellfound (AngelList) Post opportunities below or share where you found your FDE role.

The FDE Communication Playbook Technical skill gets you the job. Communication skill determines your success. The Weekly Client Update Email Send this every Friday. It is the single most impactful habit for FDEs: Template: This week: 3-5 bullet points of what was accomplished Blockers: Anything you need from the client Next week: What you plan to work on Decisions needed: Any choices the client must make Keep it under 10 lines. Executives skim. Running Client Standups Keep it to 15 minutes - Respect their time Lead with outcomes, not activities - "Users can now export reports" not "I refactored the export module" Surface blockers early - Do not wait until they become crises End with next steps and owners - Who does what by when Escalation Framework When something goes wrong (and it will): Level 1 - Informational "I want to flag that X might delay Y by a few days. I have a plan to address it." Level 2 - Need input "We have hit an issue with X. I see two options: A or B. I recommend A because of Z. Can we discuss?" Level 3 - Crisis "X is down/blocked/failing. Here is what I know, what I have tried, and what I need from you right now." Never surprise your client or your manager with bad news they should have heard earlier. Saying No Without Saying No FDEs get asked to do everything. Here is how to redirect: "We can absolutely do that. Let me scope it and show you where it fits in the timeline." "That is a great idea. Given our current priorities, should we swap it in for X or add it to the next phase?" "I can do a quick spike on that - give me 2 hours to assess feasibility before we commit." Translating Tech to Business The skill that separates good FDEs from great ones: Bad: "We need to migrate from REST to GraphQL to reduce over-fetching" Good: "We can make the dashboard load 3x faster with a backend change. It will take about a week." Always frame technical work in terms of outcomes the client cares about: speed, cost, reliability, user experience. What communication strategies have worked for you? Any templates or frameworks? Share below.

The Complete FDE Tech Stack (2026) This is the definitive reference for the tools and technologies Forward Deployed Engineers use daily. Organized by category with difficulty ratings and recommendations. Core Languages Language Use Case Priority Python Data pipelines, ML/AI, scripting, APIs Must-have SQL Data analysis, database queries, analytics Must-have JavaScript/TypeScript Demo UIs, dashboards, web apps Strongly recommended Go High-performance services, CLI tools Nice to have Rust Performance-critical systems (defense, edge) Niche but growing Java Palantir Foundry, enterprise integrations Required for Palantir Data Engineering Processing Tool What it does When to use Apache Spark / PySpark Distributed data processing Large-scale data transformations Pandas DataFrame manipulation Prototyping, small-medium datasets Polars Fast DataFrame library When Pandas is too slow dbt SQL transformation framework Analytics engineering, data modeling Apache Airflow Workflow orchestration Scheduled data pipelines Storage & Databases Tool Use case PostgreSQL Default relational database MongoDB Document storage, flexible schemas Redis Caching, real-time features Snowflake Cloud data warehouse Databricks / Delta Lake Lakehouse architecture DuckDB Local analytical queries (great for demos) Pinecone / Weaviate / Chroma Vector databases for AI/RAG AI / ML Stack Frameworks Tool Use case Priority LangChain / LangGraph LLM application framework Must-know for AI FDE LlamaIndex RAG framework Alternative to LangChain Hugging Face Transformers Model inference and fine-tuning Important OpenAI API / Anthropic API LLM access Must-know vLLM Fast LLM serving For self-hosted models MLOps Tool Use case MLflow Experiment tracking, model registry Weights & Biases Experiment tracking BentoML Model serving Label Studio Data labeling Frontend & Demo Tools The fastest ways to build impressive client-facing demos: Tool Speed Polish Best for Streamlit Fastest Medium Data dashboards, ML demos Gradio Very fast Medium AI/ML model demos Next.js Medium High Production-quality web apps Retool Fast High Internal tools, admin panels Observable / D3.js Slow Very high Complex data visualizations FastAPI Fast N/A (backend) APIs for any frontend Recommendation for FDEs Week 1 POC: Streamlit or Gradio Month 1 MVP: Next.js + FastAPI Production: Next.js + your company's design system Cloud & Infrastructure Cloud Platforms Platform FDE relevance AWS Most common. Know: EC2, S3, Lambda, ECS, RDS, SageMaker GCP Strong for AI/ML. Know: BigQuery, Vertex AI, Cloud Run Azure Enterprise-heavy. Know: Azure ML, Cosmos DB, AKS DevOps & Deployment Tool Use case Priority Docker Containerization Must-have Kubernetes Container orchestration Important Terraform Infrastructure as code Strongly recommended GitHub Actions CI/CD Standard Nginx Reverse proxy, static serving Know the basics Communication & Collaboration FDE work is 50% technical, 50% communication: Tool Use case Notion / Confluence Client-facing documentation Loom Async video updates for clients Figma Wireframing for client approvals Miro Architecture workshops Linear / Jira Project tracking Slack / Teams Daily client communication FDE-Specific Tools by Company Company Proprietary Tools Palantir Foundry, Gotham, Apollo, Code Workbook Databricks Notebooks, Unity Catalog, MLflow, Mosaic Snowflake Snowpark, Streamlit in Snowflake Salesforce Agentforce, Einstein, Platform APIs The "Day 1" FDE Toolkit If you're starting a new FDE role, set up these tools immediately: # Development brew install python node docker git pip install streamlit fastapi pandas langchain openai anthropic # Cloud CLI brew install awscli brew install --cask google-cloud-sdk # Productivity brew install gh jq httpie What tools are missing from this list? What does your FDE team use daily? Share in the replies.

FDE Work-Life Balance: An Honest Assessment Let's talk about what nobody puts in job descriptions. The FDE role offers incredible compensation and career growth — but it comes with real costs that you should understand before accepting an offer. The Data Company WLB Rating (Glassdoor) Avg Weekly Hours Travel % Burnout Risk Palantir FDSE 2.7/5 50-60 30-50% High Databricks FDE 3.5/5 45-50 20-30% Moderate Scale AI FDE 3.3/5 45-55 15-25% Moderate Anduril FDE 3.0/5 50-55 10-20% Moderate-High Stripe FDE 3.8/5 42-48 10-15% Low-Moderate MongoDB SE/FDE 3.7/5 42-48 25-35% Moderate HashiCorp FDE 4.0/5 40-45 15-20% Low The Travel Reality What "30% Travel" Actually Means On paper: ~6 days per month on the road In reality: Some months are 0%, others are 80%. It comes in waves. What drains you: Not the travel itself — it's the unpredictability. Hard to plan life when you might get sent to a client site next Tuesday. Travel Tiers by Company Type Heavy Travel (30-50%): Palantir, traditional consulting-style FDE You'll be embedded at client sites for weeks at a time Flight status and hotel points accumulate fast Hard on relationships and personal routines Moderate Travel (15-30%): Databricks, Scale AI, Datadog Monthly or bi-monthly client visits Most work done remotely with scheduled on-sites Manageable with planning Light Travel (0-15%): HashiCorp, dbt Labs, remote-first companies Mostly remote customer engagement Quarterly QBRs or kickoff meetings Best for people with families or location preferences The Burnout Factors 1. Context Switching The #1 burnout driver for FDEs isn't travel — it's context switching between clients. Typical Senior FDE load: 2-3 active client engagements simultaneously. Monday: Debug a data pipeline issue for Client A (healthcare, HIPAA constraints). Tuesday: Architecture review for Client B (fintech, real-time requirements). Wednesday: Present project update to Client C's executive team. Thursday: Back to Client A — they escalated overnight. Friday: Internal planning meeting + documentation for all three. Each client has different tech stacks, different stakeholders, different urgencies. 2. The "Hero Culture" Trap FDEs are often seen as the fixers — the ones who swoop in and save the engagement. This creates: Pressure to always be available Difficulty saying no to customer requests Scope creep that your manager won't push back on because the customer is "strategic" 3. Ownership Without Authority You're responsible for deployment success, but you don't control: The customer's infrastructure decisions Their data quality (always worse than they claimed) Internal politics at the customer org Your own product's roadmap 4. Distance from Core Product FDE work can feel disconnected from the main engineering org. Your contributions are customer-specific, making it harder to: Get recognized in engineering-wide promotions Contribute to open-source or public-facing work Build a portfolio that transfers to other companies Strategies That Actually Work Managing Travel Negotiate a travel cap in your offer. Get "max 30% travel" in writing. Batch client visits. Two clients in the same city? Schedule back-to-back. Protect anchor days. Block 2 days/week as non-travel days. Communicate this to your manager early. Invest in travel comfort. Noise-canceling headphones, TSA PreCheck, airline status, a good carry-on. These aren't luxuries — they're tools. Managing Burnout Set client communication boundaries. No Slack after 7pm. Emergency-only phone calls on weekends. Enforce this from day 1. Document everything. Reduces the "only you know how this works" trap. Makes it easier to hand off and take vacation. Rotate clients periodically. Push for 6-9 month engagement cycles, not indefinite assignments. Build internal relationships. Don't become isolated. Attend engineering all-hands, contribute to internal tools, mentor junior FDEs. Managing Career Growth Track your impact quantitatively. "Deployed system processing 50M records/day" > "Worked with Client X" Write internal blog posts about your deployments. Visibility matters for promotion. Push for speaking opportunities. Conference talks, webinars, customer case studies — these build your personal brand. Set a career timeline. Most FDEs stay 2-4 years before transitioning. Know what's next. When to Leave an FDE Role Red flags that it's time to move on: You dread Sunday evenings because of Monday client calls You haven't learned anything new in 6+ months Your manager can't articulate your promotion path You're doing more account management than engineering Travel is affecting your health or relationships Common Exit Paths Next Role Why It Works Comp Impact Senior/Staff SWE Deep IC work, no travel Lateral or -10% Engineering Manager You already manage stakeholders +10-20% Product Manager Customer empathy is your superpower Lateral Solutions Architect Same skills, less travel Lateral Startup Founder You've seen 100 customer problems Variable Field CTO Stay in FDE, go leadership +20-40% The Verdict The FDE role is one of the best career accelerators in tech — for the right person, at the right time. The comp is elite, the learning curve is steep, and the customer exposure is unmatched. But it's not sustainable for everyone long-term. The best approach: go in with clear goals, set boundaries from day 1, and plan your timeline. What's your experience? Share your WLB reality below — the honest stories help people make better career decisions.

FDE Negotiation Guide Forward Deployed Engineer offers have unique compensation components that most candidates miss during negotiation. This guide covers what to negotiate beyond base salary. What Makes FDE Negotiation Different Unlike standard SWE offers, FDE packages include components most candidates never think to negotiate: Component Typical Range Negotiable? Base salary $130K-$350K Yes, 10-15% room Equity/RSUs $40K-$350K/yr Yes, often the biggest lever Signing bonus $10K-$75K Yes Annual bonus $15K-$60K Sometimes (target %) Travel perks Varies Yes, often overlooked Security clearance bonus $30K-$80K Rarely (company policy) Relocation $5K-$50K Yes The FDE-Specific Levers 1. Equity Is Your Biggest Lever At pre-IPO companies (Scale AI, Anduril), equity can 2-5x in value. At public companies (Palantir, Databricks, Snowflake), RSU grants are real money. How to negotiate: Ask for the equity grant in dollar value, not shares. Then ask: What was the refresh policy for FDEs who performed at the top 25%? Most companies have 15-25% annual refreshers for top performers. Understanding this changes how you evaluate the initial grant. 2. Travel Policy This is the most overlooked negotiation area for FDEs: Travel cap: Get a maximum travel percentage in writing (e.g., "no more than 30%") Flight class: Business class for flights over 4 hours is standard at top companies Airline/hotel choice: Some companies let you pick your preferred airline for status Home office stipend: $1K-$5K for remote setup Travel days as work days: Ensure travel days count as work days, not personal time 3. Signing Bonus FDE signing bonuses range from $10K-$75K. Key strategies: Use competing offers: Even a verbal offer from another company gives leverage Ask for a Year 1 true-up: If equity vests over 4 years, Year 1 cash is low. A signing bonus bridges the gap. Negotiate clawback terms: Standard is 1-year clawback. Push for 6 months or pro-rated. 4. Level/Title FDE levels directly map to compensation bands. Getting leveled one step higher can mean $50K-$150K more in total comp. Bring data: Use our Salary Database to show market rates at your target level Highlight customer-facing experience: This is the differentiator. Pure SWEs don't have it. Ask about the leveling rubric: Understanding what separates Mid from Senior at that company gives you ammunition. Negotiation Script When you receive the offer: Thank you for the offer. I'm excited about the role and the team. I'd like to discuss a few components before I sign. Based on my research and competing opportunities, I was expecting total compensation closer to [TARGET]. I'm flexible on how we get there -- whether through base, equity, or signing bonus. I'd also like to discuss the travel policy. Can we align on a maximum travel expectation of [X]%? And I'd appreciate business class for cross-country flights given the frequency of travel in this role. What NOT to Do Don't give a number first. Let them make the offer. Don't say "I need to think about it" without asking questions. Ask questions in the same call. Don't negotiate over email if you can do it on a call. Tone matters. Don't bluff about competing offers you don't have. Recruiters talk to each other. Compensation by Negotiation Outcome Scenario Typical Total Comp After Negotiation Junior FDE (new grad) $160K $180K-$200K Mid FDE (3-4 YOE) $250K $280K-$320K Senior FDE (5-8 YOE) $350K $400K-$450K Staff/Lead (8+ YOE) $450K $500K-$600K The difference between negotiating and not negotiating is typically 15-25% in total comp. Over a 3-year stint, that's $100K-$300K left on the table. When to Walk Away Red flags in the offer stage: Company refuses to share equity details (strike price, valuation, vesting) Travel expectations are vague and they resist putting a cap in writing Leveling feels low and they won't explain the rubric Base is below market and they only offer "upside" via equity at an early-stage company Negotiated an FDE offer recently? Share your data point (anonymously) in the replies and in our Salary Database. Real data helps everyone negotiate better.

The Forward Deployed AI Engineer The AI FDE is the fastest-growing variant of the Forward Deployed Engineer role. As enterprises race to deploy AI, they need engineers who can take models from demo to production in customer environments. This guide covers everything you need to know. What Makes an AI FDE Different Traditional FDE AI FDE Core work Data platform deployment, integrations LLM deployment, RAG, AI agent building Tech stack Python, SQL, Spark, cloud Python, LangChain, vector DBs, model serving Customer ask "Help us use our data better" "Help us deploy AI that actually works" Key challenge Data quality, integration complexity Hallucinations, evaluation, cost management Comp premium Baseline FDE comp 10-20% premium over traditional FDE Who Is Hiring AI FDEs Tier 1: AI-Native Companies Company Role Comp Range Focus Anthropic FDE / Solutions Eng $250K-$600K Claude enterprise deployment OpenAI Solutions Eng / FDE $280K-$700K GPT deployment, fine-tuning Databricks AI FDE $250K-$440K Mosaic, MLflow, model training Scale AI FD AI Engineer $190K-$400K Data labeling, RLHF, evaluation Cohere FDE $150K-$280K Enterprise LLM deployment Tier 2: Platform Companies Adding AI FDE Company Role Focus Salesforce Agentforce FDE AI agent deployment Palantir FDSE (AIP) Palantir AIP deployment Snowflake AI FDE Cortex AI features Datadog ML Solutions Eng AI observability The AI FDE Tech Stack Must-Know LLM APIs: OpenAI, Anthropic, Google (Gemini), open-source (Llama, Mistral) RAG Frameworks: LangChain, LlamaIndex, Haystack Vector Databases: Pinecone, Weaviate, Chroma, pgvector Prompt Engineering: System prompts, few-shot, chain-of-thought Evaluation: Custom evals, LLM-as-judge, retrieval metrics (MRR, recall@k) Should Know Agent Frameworks: LangGraph, CrewAI, AutoGen Fine-Tuning: LoRA, QLoRA, PEFT Model Serving: vLLM, TGI, Triton, SageMaker endpoints Embeddings: Sentence transformers, OpenAI embeddings, Cohere embed Guardrails: Content filtering, PII detection, output validation Emerging Multi-modal AI: Vision + language models for document processing Voice AI: Real-time speech-to-text + LLM + text-to-speech AI Agents in Production: Tool use, function calling, autonomous workflows What AI FDE Deployments Actually Look Like Engagement 1: Enterprise RAG (Most Common) Customer: Fortune 500 financial services firm Problem: 500 analysts spending 2 hours/day searching internal documents Solution: Ingest 2M documents (PDFs, emails, reports) into vector database Build retrieval pipeline with hybrid search (BM25 + semantic) Deploy chat interface with citations and source linking Custom evaluation pipeline: retrieval accuracy, answer quality, hallucination rate Timeline: 8 weeks to production Result: 60% reduction in research time, 85% user satisfaction Engagement 2: AI Agent for Operations Customer: Manufacturing company Problem: Factory floor managers spending 3 hours/day on reporting and data entry Solution: Build AI agent that can query production databases via natural language Function calling for: inventory checks, quality reports, shift scheduling Guardrails to prevent data modification without human approval Slack integration for natural interaction Timeline: 6 weeks to pilot Challenge: Ensuring agent doesn't hallucinate production numbers Engagement 3: Customer Support Automation Customer: SaaS company with 50K monthly support tickets Problem: 70% of tickets are repetitive, L1 agents burning out Solution: Fine-tune model on historical ticket resolution data RAG over knowledge base and product documentation Confidence scoring — auto-resolve high-confidence, escalate low-confidence Human-in-the-loop review for edge cases Timeline: 10 weeks to production Result: 45% auto-resolution rate in month 1, 62% by month 3 Common Failure Modes (and How to Avoid Them) Failure Mode Root Cause Prevention Hallucinated answers No retrieval grounding, no guardrails Always use RAG, implement citation checking Poor retrieval quality Bad chunking, wrong embedding model Test chunking strategies, evaluate retrieval independently Cost explosion Sending too much context, no caching Implement prompt caching, optimize chunk selection Slow responses Large context windows, no streaming Stream responses, async processing, response caching Customer distrust No explainability, black-box answers Always show sources, confidence scores, human escalation path AI FDE Interview: What Is Different Standard FDE interview + these AI-specific components: AI System Design Round "Design a RAG system for a legal firm with 10M documents" "How would you build an AI agent that can query databases safely?" What they're looking for: Practical architecture, awareness of failure modes, evaluation strategy AI Technical Deep-Dive "Explain how retrieval-augmented generation works end to end" "What's the difference between fine-tuning and RAG? When do you use each?" "How do you evaluate an LLM application in production?" AI Case Study "A customer's RAG system is returning wrong answers 20% of the time. How do you debug this?" "The CEO wants to deploy an AI chatbot for their customers by next month. What do you do in week 1?" How to Prepare for AI FDE Roles 30-Day Plan Week 1: Build a RAG application end-to-end (document ingestion → retrieval → generation → evaluation) Week 2: Add an AI agent with function calling (database queries, API calls) Week 3: Deploy to cloud with proper monitoring (latency, cost, quality metrics) Week 4: Build an evaluation pipeline (retrieval quality, answer quality, hallucination detection) Portfolio Project Ideas Legal document Q&A — RAG over case law with citations Code review agent — AI that reviews PRs and suggests improvements Customer support bot — Train on your own documentation, measure resolution rate Data analyst agent — Natural language to SQL with guardrails Working as an AI FDE? Share what tools and patterns are actually working in production. The community needs real-world signal, not Twitter hype.

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