Need a full-stack engineer who still ships production code? I own the path from browser to database: Next.js or React, FastAPI or Django, Postgres, Redis, workers, and deploys — plus LLM, RAG, and tool-calling when your product needs them. Same ownership whether the backlog is classic SaaS or AI-first, so you do not stall when a model works in a demo but not next to billing. For multi-agent orchestration and LangGraph depth, see the agentic AI developer page; this one stays on the full product slice.
How I staff and ship the role in practice
A full-stack engineer takes a product idea from sketch to something users can touch: UI and interaction on the client, validation and business rules on the server, durable state in a database, plus logging, metrics, migrations, and deploys so incidents stay boring. When teams hire for this role, they usually want fewer handoffs between “frontend” and “backend” when the calendar is tight.
That does not mean one person replaces specialists forever. It means I can pair with design on tricky UX, with platform when networking matters, and still own the PR that wires a new table through the API into the UI. Remote work makes this sharper: hallway chat disappears, so OpenAPI contracts, typed clients, and short ADRs become how we stay aligned.
Accessible UI in React or Next.js, REST (or GraphQL) with clear errors, SQL that holds up as data grows, tests at the bar your org actually enforces, and on-call or incident review when you need it. The gap between a weak hire and a strong one shows up under pressure: Postgres locks, cache stampedes, dependency timeouts. I reach for traces and root cause, not blind pod restarts.
Titles blur — full-stack developer vs engineer. Write down what you need this quarter: fast UI with light API work, or cross-service refactors, migrations, and performance. I lean engineer — systems judgment, backwards-compatible APIs, cost-aware queries — and still ship pixels when the roadmap asks for it.
Job posts often add “AI” to the title. In production, models are just another dependency: timeouts, budgets, structured outputs, traces. Streaming chat and retrieval sit beside forms and dashboards. Someone has to own how tokens cross the gateway, how per-tenant limits behave, and how support replays a failed run from logs.
If you need classic product velocity now and copilots later, say so up front. We can start as straight full-stack work and add LLM surfaces once auth and data boundaries are ready. Already agent-heavy? Use the agentic AI developer page for orchestration; I still own APIs and persistence the same way here.
Same vertical slice — with models meeting users and internal systems
Teams add “AI” when the hire must own where models meet the product: web clients, API gateways, relational data, caches, background jobs, streaming token UX, and admin for prompts or evals. That is the role you need when a prompt specialist is not enough — nothing streams end to end, traces are missing, and finance asks why credits are wrong.
Lead scope adds coordination: break epics into shippable pieces, keep API contracts stable for web and mobile, guard release hygiene, and put AI routes through the same security review as checkout. That can be mostly hands-on with light steering, or advisory next to your staff. The constant is judgment across layers, not ticket churn.
Agentic AI is a specialization: planners, tool registries, agent handoffs, eval suites, and blast-radius controls when models take actions. I keep a separate agentic AI developer page for buyers who already need loops, not just chat. On most roadmaps, full-stack delivery and agentic depth are sequential milestones on the same codebase — one person who speaks both languages cuts integration risk.
Startups often post “full stack + OpenAI” before they have words for RAG vs agents. Enterprises say “lead full stack” when they need someone who can stand up services and pilot a copilot. The real ask is the same: ship software where the model is a component you can operate, cost, and audit — not a black box behind a demo button.
On my portfolio timeline, WinstaAI is AI-first SaaS where billing, admin, streaming UX, and model routing have to coexist. When a gateway bug breaks credits, or retrieval drifts because chunking never matched real PDFs, you want one accountable path from browser to vector index.
Practical signals I look for (and offer): eval loops before launch, a clear story for idempotent model-triggered writes, and token cost per tenant next to HTTP p95. Those rarely appear as résumé keywords, but they predict on-call pain after real traffic.
For schedules, queues, and webhooks around the same product, see business automation. For microservices and deploy patterns, pair this with the Microservices architecture and the stack overview.
Illustrative layers; your cloud and compliance rules may swap parts
I use this table with CTOs and founding engineers before we write a SOW. Each row is a place where “it works in Postman” can still fail in production if nobody owns the full path. On an AI roadmap I am accountable for contracts between rows — not only the LLM cell, and not only the React tree without the migrations behind it.
Tooling varies; the jobs stay the same.
| Layer | Common choices | What I typically own |
|---|---|---|
| Client | Next.js, React, React Native WebView | Streaming chat UX, optimistic UI for safe actions, auth sessions |
| API | FastAPI, Django REST, Node (Express/Fastify) | Versioned routes, rate limits, structured logging, OpenAPI for mobile |
| AI services | OpenAI, Anthropic, Gemini, local inference | Routing, fallbacks, cost caps, JSON schema checks before side effects |
| Retrieval | PgVector, OpenSearch, managed vector DB | Chunking, rerank, tenant isolation, offline eval hooks |
| Data plane | PostgreSQL, Redis, object storage | Migrations, idempotency keys, cache semantics, redacted audit trails |
| Delivery | Docker, GitHub Actions, cloud load balancers | Staging parity, feature flags, dashboards for errors and token spend |
Architecture and review culture without losing keyboard time
Many teams use lead full-stack developer to mean “owns architecture and still commits to critical paths.” That matches how I like to work: set non-negotiables (auth model, error taxonomy, release checklist), unblock others on tricky SQL or async bugs, and keep AI surfaces from bypassing those rules.
Concrete lead deliverables can include API style guides, PR templates with security checks for new tools, pairing on the first multi-tenant RAG index, and runbooks for model upgrades. I am not a substitute for a full engineering-manager roster, but I help when you need senior execution and technical steering until permanent leads land in each timezone.
If the mandate is pure people management with little IC work, say so up front — I can point you to collaborators and stay on the hardest build tracks instead.
Classic product work and AI features, same delivery bar
1 · Map the product surface. User flows, existing APIs, auth, data classes, and where intelligence helps versus where fixed workflows should stay. Latency budgets and cost envelopes come before model selection so you do not lock into a vendor on day three.
2 · Vertical slice. One path through UI, API, persistence, and a single LLM or retrieval flow — with structured outputs, traces, and a thin operator toggle where useful. The slice should fail in staging the same ways production will, not only in notebooks.
3 · Harden the stack. Workers for long chains, idempotent writes, per-tenant limits, CI for migrations and eval fixtures, and dashboards that include token spend next to p95 latency. This is where a prototype becomes something SRE and security can sign off.
4 · Evaluate and iterate. Golden tasks for prompts and tools, regression when schemas change, sampling on live traffic, and periodic pruning of unused tools or dead prompts.
When the roadmap shifts into multi-agent orchestration or LangGraph-heavy graphs, we either add explicit agentic milestones or follow the playbook on agentic AI developer so docs stay honest about which approach applies.
Each card is a concrete outcome. WhatsApp opens with that topic already filled in.
Ship user-visible work with stable contracts — Next.js or React, FastAPI or Django, Postgres migrations, and tests at your bar.
Milestones or retainer · scoping call first
Streaming responses, structured JSON, provider routing, fallbacks, and server-side checks so the client never trusts raw model text for business logic.
Works well when APIs already exist
Chunking, hybrid search, rerank, citation rules, and evals so quality holds when the corpus grows from demo size to customer size.
Often paired with freelancer services
Lead voice when you need it: milestone planning, risk lists for AI features, review culture, and alignment with security and finance.
Advisory plus IC mix — scope explicitly
Credits, subscriptions, per-workspace limits, operator dashboards — the rails that keep AI features from becoming a margin leak.
Phased to your release train
Tool schemas, idempotent side effects, and tracing so when you move from chat to agentic AI, the foundation is already production-shaped.
Pairs with the agentic page for orchestration depth
Straight answers. Schema markup matches what you see here.
I own features across client, server, and data: UI, APIs, schema design and migrations, caches and background workers, plus logs and metrics so operators can debug real traffic. On SaaS teams that also means auth sessions, rate limits, deploy pipelines, and working with design and product so releases stay predictable.
Employers often use the titles interchangeably. “Engineer” sometimes signals more systems thinking across services; “developer” sometimes emphasizes UI velocity. What matters is a written scope: front-end framework, API ownership, database design, testing, and ops maturity — not the noun on the business card.
Depth in at least one web framework (React or Next.js), one strong backend (Python with FastAPI or Django, or Node), SQL beyond simple selects, queues or async jobs when work exceeds request timeouts, and CI/CD and review habits that match your risk. For AI products, add structured outputs, eval hooks, and cost-aware model routing.
I treat models as services with contracts: schema-checked responses, server-side business rules, separate read and write paths, tracing and sampling, and budgets for latency and token spend. The same person who owns checkout should own how streamed tokens reach the browser and how failures show up for support — not a disconnected notebook demo.
For multi-agent orchestration, tool-calling loops, LangGraph versus custom Python, and production guardrails, use the agentic AI developer page. This page covers the broader full-stack product context and links there for deep agent work.
Queues, schedules, and webhooks keep money and data moving reliably; AI layers interpret intent and choose actions. Good systems use both. See business automation for durable workflow patterns that pair with LLM features.
Mention your stack, rough traffic, and whether you need senior IC delivery, lead coverage, or both.
Next.js, FastAPI, LLM/RAG features — clear pricing when you hire me without an agency.
💡 Direct clients only: These prices are for hiring me straight — no agency cut. They already include my current 50% discount.
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