Services / Python AI Development
Python AI Development Consultant | Production Data Pipelines & ML Infrastructure
Python machine learning consultant specializing in RAG ingestion, ML model deployment, and data engineering. I work with PyTorch/LoRA fine-tuning, local inference clusters, and FastAPI backends that integrate LangChain, LangGraph, and Cognee workflows—so your data infrastructure scales without surprises.
Typical delivery
6-8 weeks · Weekly checkpoints · Python backend + data pipelines + security testing
Timeline includes:
- Weeks 1-2: Data audit, schema design, ingestion scaffolding
- Weeks 3-4: FastAPI services, Celery/Temporal workflows, pgvector tuning
- Weeks 5-6: LLM orchestration, eval harness, observability
- Weeks 7-8: Pen testing, load testing, runbooks, production cutover
Deliverable: Secure, observable Python backend powering AI platforms with documentation + compliance artifacts.
Related services
Python backends pair with these services for complete AI platforms.
Python stack trusted by production AI teams
PyTorch ecosystem
PyTorch training loops, LoRA adapters, ONNX conversion, and TorchServe deployment—the same stack used by major AI labs for production model serving.
FastAPI + Pydantic
Type-safe APIs with automatic OpenAPI docs, dependency injection, and async support—chosen by Netflix, Uber, and Microsoft for high-performance Python services.
Celery + Temporal
Battle-tested workflow orchestration used by Robinhood, Airbnb, and Stripe for reliable background processing and data pipelines at scale.
Why Python for AI platforms?
Python dominates AI infrastructure for good reasons: mature ML libraries, fast prototyping, and a massive ecosystem. Here's when to choose Python over other backend stacks.
Best for data-heavy workloads
PyTorch, NumPy, Pandas, and scikit-learn are Python-native. If your platform involves model training, feature engineering, or data transformations, Python is the default.
When to pair Python + TypeScript
Python for data pipelines and ML inference, TypeScript/Next.js for user-facing APIs and dashboards. This gives you the best of both worlds.
Python vs Node.js for AI
Node.js works for calling hosted AI APIs (OpenAI, Anthropic), but Python dominates for custom models, data engineering, and compute-heavy workloads.
When to use Python vs. TypeScript for AI backends
Choose Python when you need:
- Model training (PyTorch, TensorFlow)
- Heavy data transformations (Pandas, NumPy)
- Custom ML pipelines or LoRA adapters
- Scientific computing libraries
Choose TypeScript when you need:
- Calling hosted AI APIs (OpenAI, Anthropic)
- Real-time user interactions and dashboards
- Frontend + backend monorepos
- Vercel Edge runtime optimization
Most production AI platforms use both: Python for data-heavy workloads, TypeScript for user-facing APIs.
What's included
Python backend + data engineering without handoffs
15 years shipping production platforms with Python at the core. As a Python AI consultant and ML engineer, I work across the full backend stack—from data ingestion and ETL workflows to API design and security testing. Whether you need FastAPI services, Celery pipelines, or PyTorch model deployment, I collaborate with your team or handle the Next.js frontend if you need full-stack coverage.
- RAG ingestion pipelines (pymupdf, unstructured.io, LangChain)
- FastAPI/Django REST/Flask services with Pydantic + RBAC
- Celery/Temporal workers for ETL, retraining, evaluations
- Postgres/pgvector schemas with hybrid retrieval SQL
- Model deployment + fine-tuning (PyTorch, LoRA, OpenAI/Anthropic, TorchServe, ONNX)
Cross-functional delivery
Backend + frontend need to evolve together. This Python service pairs with the Next.js platform offering, so dashboards, admin panels, and customer experiences stay in sync with your data pipelines.
Engagement model
- • 6-8 week delivery, extended retainers as needed
- • Weekly working sessions + Loom summaries
- • Staging env live by Week 2 for stakeholders
- • Security + testing embedded in every sprint
- • Cancel anytime with two weeks notice
Python stack deliverables
Everything required to ship a production-grade Python backend: ingestion jobs, APIs, orchestration, model deployment, and the testing artifacts that prove it's secure.
Data engineering patterns
Choose the right approach for your ingestion and orchestration stack. These patterns keep data flowing without sacrificing observability or governance.
Model deployment & inference
From managed APIs like OpenAI/Anthropic to self-hosted TorchServe or ONNX Runtime, deployments include caching, cost controls, and observability.
Testing infrastructure that prevents regressions
Backend services and pipelines get the same treatment as customer-facing apps: automated testing, security scanning, and CI/CD gates before launch.
- Unit + integration tests for services, workers, and data transforms
- Coverage reports enforced in CI with thresholds (80%+)
- Factories + fixtures for deterministic test data
- Snapshot tests for serialized payloads
- Property-based tests catch edge cases across ingestion + parsing
- Locust/k6 load tests validate throughput and scaling assumptions
- Synthetic tenants ensure RLS rules hold under concurrency
- Alerting wired to performance budgets
- OWASP ZAP, Nuclei, and custom prompt-injection suites
- Dependency scanning (pip-audit, Snyk) with auto patch suggestions
- Secrets detection and policy enforcement in CI
- Rate limit and abuse prevention regression tests
- GitHub Actions / GitLab CI pipelines running lint + tests on every PR
- Dockerized preview environments for manual validation
- Slack / email alerts for failures and flaky tests
- Blue/green deploys with automatic rollback triggers
Why automated testing matters
Pipelines break when schemas shift or prompts drift. Automated pytest suites, Hypothesis fuzzing, and penetration testing catch those failures before customers do—and give investors confidence that the stack is stable.
Process
Python AI consultant process
I handle discovery, ingestion, orchestration, and security testing without multi-vendor handoffs. Weekly checkpoints keep you in sync, and code ships iteratively.
- 01 – Discovery & data audit. Inventory sources, compliance constraints, and ingestion volume. Define golden datasets.
- 02 – Architecture & ingestion. Stand up FastAPI/Django services, ETL workers, and pgvector schemas with monitoring.
- 03 – Retrieval & orchestration. Implement semantic search, hybrid scoring, Celery/Temporal workflows, and eval harnesses.
- 04 – Hardening & launch. Pen testing, pytest + load testing, runbooks, and handover docs mapped to required controls.
Need to assess data readiness first? Review our data engineering patterns .
- Row-level security and tenancy context enforced at the database + API layers.
- Prompt injection sanitizers for outbound tool usage.
- Eval harnesses with golden question sets and drift detection.
- PostHog instrumentation for ingestion health and cost monitoring.
- Pen testing + prompt testing documented before launch.
- Python 3.12+
- FastAPI / Django REST Framework / Flask
- Celery / Temporal / Prefect
- Postgres + pgvector / Redis
- Pydantic / SQLAlchemy
- LangChain / LlamaIndex
- PyTorch / LoRA / ONNX / TorchServe / OpenAI / Anthropic
- Docker / Terraform / GitHub Actions
- Vercel / Render / AWS deploys with zero-downtime rollouts
- Feature flags for progressive releases
- Database migrations with rollback + seeding plans
- Monitoring dashboards for ingestion, latency, and errors
- Runbooks + on-call handover for your team
Recent builds & outcomes
Proof this model works
Same case studies, different angle: these wins required Python services powering RAG pipelines, agent workflows, and modernization.
Fintech RAG Launch
6 critical vulns patched pre-audit
- Multi-tenant RAG had tenant isolation bug leaking customer queries across accounts—caught during Week 4 pen testing.
- Missing rate limits on inference APIs would have enabled token-spend DoS—fixed before onboarding banks.
- Passed bank security review on the first attempt with zero findings.
- Zero delays to launch timeline—shipped Week 8 on schedule.
Agent Operations Platform
25 workflows live, 0 security regressions
- MCP servers with tool allowlists + audit logging shipped Week 6—sandboxed execution for untrusted tools.
- Credential misuse caught during testing when an agent attempted prod DB access—blocked by the policy engine.
- Pen testing + retests baked into every rollout checkpoint.
- Adoption + cost dashboards ready for Series A investor demos.
Multi-tenant SaaS Modernization
Zero findings on external pen test
- Rails/Next.js + Python services upgrade with RAG knowledge base and Stripe usage metering.
- 800+ automated tests + continuous eval harnesses caught regressions before production.
- External penetration test (required by enterprise customers) closed with zero findings.
- Support backlog dropped 40% once AI summaries + regression prevention landed.
FAQ
Common questions
Honest answers to the questions Python and data teams ask before kicking off.
What Python work do you handle?
FastAPI and Django services, ETL pipelines (Celery/Temporal), pgvector schema design, model deployment (TorchServe/ONNX/OpenAI), and the security testing that proves everything is production-ready. I live and breathe Python data workflows, so every ingestion job, API endpoint, and eval harness gets the same attention.
Can you own both backend and frontend?
Yes. This page covers the Python + data engineering stack. Need dashboards or customer-facing UX? My Next.js AI platform development service handles that—same engineer, no handoffs.
Do you integrate with existing data warehouses?
Absolutely. I connect to Snowflake, BigQuery, Redshift, or on-prem Postgres, then build ingestion + transformation jobs that respect existing governance and auditing policies.
Are you a Python ML engineer or consultant?
Both. I work as a Python machine learning consultant for strategy and architecture, and as a hands-on Python ML engineer when building pipelines, training models, and deploying inference services. Most engagements need both.
How do you test Python pipelines?
pytest for unit/integration tests, Hypothesis for property-based testing, load tests for throughput, and OWASP ZAP/prompt-injection suites for security. Every PR runs through CI with coverage gates.
Do you work with in-house teams?
Yes—most engagements pair me with a founder, staff engineer, or data team. I ship the backbone services, document everything, and stay on call for handoff or retained engagements.
Need Python infrastructure for your AI platform?
Whether you're layering AI into an existing product or building from scratch, you work directly with an engineer who ships FastAPI services, data pipelines, and security testing together.
Serving companies across the San Francisco Bay Area, Silicon Valley, and remote teams worldwide.