Artificial Intelligence & Data Science · 1.6

Forward Deployed Engineer

Bridge the gap between AI products and customer success through technical implementation, customization, and on-site problem-solving.

6–9 months5 phases18 skills

Phase 01 · 23h

Software Engineering Foundations

Python backend

Python backendFastAPI (REST API endpoints, OAuth2 authentication, request validation with Pydantic, background tasks, WebSocket support), Node.js with Express.js (async event loop, middleware pattern, basic API to understand the ecosystem)

Frontend

FrontendReact basics (components, props, state, hooks — useState, useEffect, event handling), Tailwind CSS for rapid styling, fetching data from APIs with axios, building dashboards with Recharts (LineChart, BarChart, PieChart)

Databases across types

Databases across typesPostgreSQL (relational — complex queries, indexing, performance tuning), MongoDB (document database — store and query JSON documents with pymongo, use for flexible-schema customer data), Redis (in-memory key-value — caching with TTL, session management, rate limiting with redis-py), Elasticsearch (full-text search with relevance scoring for building search features)

Cloud and DevOps

Cloud and DevOpsAWS core services (EC2, S3, RDS, Lambda, API Gateway, CloudWatch, IAM), containerize with Docker, deploy on ECS Fargate with ALB, CI/CD with GitHub Actions, monitoring with Datadog or New Relic for application performance

Version control and testing

Version control and testingGit workflows with meaningful branches and PR reviews, unit/integration/E2E testing

Phase 02 · 5h

AI/ML Literacy

ML concepts (understand, not build from scratch)

ML concepts (understand, not build from scratch)supervised vs unsupervised, classification vs regression, overfitting and regularization, evaluation metrics (accuracy, precision, recall, AUC), LLM fundamentals (transformers conceptually, prompt engineering, fine-tuning vs RAG, context windows, token costs)

Data integration patterns

Data integration patternsETL/ELT workflows, connecting to customer data sources (databases via connectors, REST APIs with pagination and auth, CSV/SFTP file exchange — still common in enterprise), data quality issues (missing data, duplicates, schema mismatches), PII handling and data privacy requirements

Integration architecture

Integration architectureAPI integration (customer app calls your API), embedded UI (iframe/web component), data pipeline integration (batch uploads, streaming, CDC), webhook/event-driven (your platform sends events to customer systems)

Phase 03 · 8h

Customer-Facing Technical Skills

Technical discovery

Technical discoverypre-meeting research (customer industry, tech stack from job postings), discovery meeting structure (business problem → current architecture → data sources → performance requirements → security/compliance needs → success criteria), writing structured technical discovery documents

Architecture design

Architecture designcreate clear system diagrams with draw.io or Excalidraw showing data flow, authentication, network boundaries, and failure points; write technical design documents (components, API contracts, error handling, security, monitoring, rollback plan)

Custom solution development

Custom solution developmentrapid prototyping (working demos in 1–3 days with Streamlit for data apps, React + FastAPI for web apps), custom data pipelines for customer-specific formats, customer-specific analytics dashboards with Plotly Dash, configuration-driven design (YAML/JSON config over hardcoded logic for scalability)

Implementation management

Implementation managementbreak into milestones (Week 1: data integration, Week 2–3: model customization, Week 4: dashboard, Week 5: testing/go-live), weekly status updates, bi-weekly technical syncs, handling scope creep with trade-off communication

Phase 04 · 6h

Product & Business Skills

LLM customization for customers

LLM customization for customerssystem prompts incorporating customer context (company name, product catalog, brand voice, compliance rules), RAG implementation with customer-specific knowledge base (ingest their documentation, build vector search), evaluation sets built collaboratively with the customer (50–100 examples of ideal input-output pairs)

Enterprise security

Enterprise securitySOC 2 Type II compliance (access management, encryption, monitoring, incident response), HIPAA for healthcare (BAA, encryption at rest/transit, audit logs), GDPR for EU data (DPAs, right to deletion, data portability), VPC deployment for customers requiring on-premises or private cloud operation

Technical communication

Technical communicationdocumentation for engineers (API refs, architecture docs), executive summaries for leaders (business impact, ROI, timeline), user guides for end users (step-by-step with screenshots), demo presentations, case studies

Phase 05 · 13h

Capstone

Capstone Project 1

Capstone Project 1Simulate an enterprise integration — discovery document, architecture diagram, build a RAG system with customer-specific knowledge base + FastAPI backend + React dashboard, deploy with Docker, record a 15-minute demo video

Capstone Project 2

Capstone Project 2Build a data pipeline that ingests messy customer data (CSVs with deliberate quality issues), profiles/cleans with Great Expectations, transforms for ML, validates at each step, loads into a feature store — with error handling, alerting, and monitoring

Capstone Project 3

Capstone Project 3Write a complete technical proposal (executive summary, problem statement, proposed solution with architecture diagram, timeline, pricing model, risk mitigation, success criteria) and present in a 20-minute mock sales engineering call

Ready to start Forward Deployed Engineer?

Start this roadmap to track progress and generate courses for each skill. Login required only to begin.