Python Engineering for Data and Backend Work
We build Python services and data systems that don't rot in a Jupyter notebook. Typed code, real packaging, real tests, real deployments — FastAPI APIs, Django apps, Airflow pipelines, ML inference services, and the boring data plumbing that actually keeps a business running. Honest about when Python wins and when it doesn't.
You don't need another notebook.
You need Python you can deploy.
Most Python projects die in the gap between research and production. The notebook works on the data scientist's laptop, then takes six months to turn into a service nobody trusts. We close that gap: typed code, packaged properly, tested properly, deployed properly, and observable in production. The same discipline whether it's a FastAPI service, a Django monolith, or an Airflow DAG that the finance team depends on.
It only runs in one specific Jupyter kernel
No requirements file, no version pinning, half the imports come from the global conda env, and the other half from a folder on the data scientist's desktop. Reproducible? Not even on the same laptop next month.
The pipeline takes 6 hours and nobody knows where
A pandas operation that should be a SQL query, a for-loop calling an API one row at a time, no parallelism, no profiling, no caching. The job runs overnight and silently fails twice a week.
No types, no tests, no idea what breaks
Functions that take Any and return Any, dictionaries with secret schemas, refactors that break things three modules away. The team is afraid to upgrade pandas because nobody knows what depends on which deprecated method.
What You Actually Get
No vague deliverables. Here's exactly what lands in your hands.
A real, packaged Python project
pyproject.toml, locked dependencies (uv or Poetry), src/ layout, ruff + mypy/pyright in CI, semantic versioning, a Dockerfile that actually works on Linux. Shippable from day one.
Tests at three layers
pytest unit tests, integration tests against a real database in Docker, end-to-end tests for APIs and pipelines. Coverage thresholds enforced. PRs blocked when coverage drops.
Real observability
Structured logs (structlog), OpenTelemetry traces, Prometheus metrics, Sentry error tracking. Pipelines emit metrics per stage so you know exactly which step is slow or failing.
Source, infra and runbooks in your name
Your GitHub, your AWS/GCP account, your secret store, your runbooks in your wiki. We hand over a service your next engineer can on-call from day one.
A Real Python Engineering Team
Shipping Python well takes more than one data scientist with a notebook. Six roles you get on every Pillai Infotech Python build.
Senior Python Engineer
Type hints, async/await, packaging, profiling, C extensions when needed. Has read CPython source for fun and knows why your dict iteration is suddenly slow.
Database & Schema Lead
PostgreSQL deep, SQLAlchemy 2.0, Alembic migrations, indexes, query plans, partitioning. Owns the schema so the ORM stops surprising the team.
Data Engineering Lead
Airflow, Prefect, Dagster, dbt, Spark when justified. Designs idempotent, retryable, observable pipelines. The engineer who cares what happens when a job re-runs.
ML / LLM Engineering Lead
PyTorch, scikit-learn, Hugging Face, LangChain when it earns its place, vector DBs, model serving with vLLM, BentoML or FastAPI. Knows the difference between a notebook result and a production model.
Security & Auth Lead
OAuth2/OIDC, secure secret handling, input validation with Pydantic, dependency scanning, OWASP. Files an issue when your endpoint trusts a JWT without verifying it.
DevOps / Platform Lead
Docker, CI/CD, Kubernetes when justified, blue-green or canary deploys, zero-downtime migrations, health checks. Makes the ops side boring so engineering can focus on product.
You See Everything. In Real Time.
Every Pillai Infotech project comes with a dedicated client dashboard. Kanban boards, live logs, test results, meeting notes — it's all visible the moment it happens. No status-report theatre, no "we'll get back to you", no surprises at the demo. You work with us like you work with your own team.
Kanban Board, Live
Every epic, every story, every task — visible on your dashboard. Drag, comment, reprioritize. It's the same board our team works from.
Documented Everything
Every decision, spec, API contract, and architecture diagram lives in the dashboard. Searchable, versioned, linked to the tasks they shaped.
Live Logs & Test Results
Build logs, deployment logs, test suite results — streamed to your dashboard the moment they run. You never have to ask "did the build pass?"
Meetings → Tasks, Automatically
Every meeting is recorded, transcribed, and every action point is auto-converted into a tracked task assigned to the right person. Nothing gets lost between calls.
Sprint Burndown & Velocity
See exactly how much work is done, how much remains, and our velocity over time. If a sprint is slipping, you see it the same moment we do.
Comment, Approve, Decide — In-Place
Comment on any task, approve designs, sign off on specs, and raise blockers directly in the dashboard. Everything tied to the work, not buried in email threads.
Python Systems We Know How to Ship
We pick the right Python shape for the workload — API, monolith, pipeline, worker, or model server.
⚡ FastAPI services
Async APIs with Pydantic v2, OpenAPI generation, dependency injection done right, background tasks, WebSockets. The default for modern Python APIs and we've shipped a lot of them.
🏛️ Django applications
Django + DRF for content-heavy, admin-heavy, RBAC-heavy products. Boring, mature, productive. Often the right call for B2B SaaS even when Twitter says otherwise.
🛠️ Data pipelines & ETL
Airflow, Prefect or Dagster DAGs that are idempotent, observable and retryable. dbt for warehouse transforms. The kind of pipeline that survives schema changes upstream.
🤖 ML & LLM serving
Model APIs with FastAPI or BentoML, batching, GPU scheduling, prompt caching, RAG with pgvector / Qdrant / Weaviate, eval harnesses. Cost and latency aware from day one.
📊 Analytics & internal tools
Streamlit or Dash dashboards for internal teams, Jupyter-to-production conversions, finance and ops automation. The unsexy work that quietly removes 20 hours a week from someone's job.
🔌 Integration & automation services
Webhook receivers, third-party API glue (Stripe, HubSpot, Salesforce, Shopify), scheduled jobs, idempotent retries. Python is excellent at this and we lean into it.
The Python Stack We Use
Modern, typed, packaged. We've left the Python 2 / setup.py / virtualenvwrapper era behind.
Core & Frameworks
Tooling
Data & ML
Quality & Ops
A Six-Stage Python Delivery Process
Built to close the gap between notebook and production.
Discovery & Architecture Call
Workload shape, expected traffic or data volume, latency or throughput budgets, integration list, deployment target. Decided in week one, in writing, with trade-offs.
Foundation Sprint
pyproject.toml, locked deps, src/ layout, ruff, mypy/pyright, pytest, Dockerfile, CI, structured logging, error tracking, Alembic migrations. Two weeks of plumbing that pays back forever.
Vertical Slice Builds
One end-to-end endpoint or pipeline shipped per sprint with tests, docs, observability and a profile run. No "we'll add monitoring later".
Performance & Failure Testing
Profiling with py-spy or Scalene, load tests with Locust or k6, chaos drills (kill the DB, kill the worker), backpressure tests. Find the breakpoints before users do.
Hardening & Launch
Security review, secret rotation, dependency scan, SLO definition, alert thresholds, runbook for the top 5 incidents. Then we ship.
Post-Launch Monitoring
Weekly review of latency, error rate, pipeline success rate, DB health, cost. On-call rota or warranty period agreed. Hotfix and rollback rehearsed in the first week.
Three Ways to Engage
Python projects come in different shapes. Pick the one that matches your stage.
Python Architecture Sprint
Two-week engagement to audit your existing Python service or pipeline, or design a new one — packaging, schema, observability, deployment — and ship a written plan.
- Architecture decision record
- Performance + security audit
- Honest build estimate in writing
Fixed-Scope Python Build
End-to-end Python delivery from spec to production, with database, integrations, observability, and post-launch warranty.
- Fixed scope, fixed price
- Typical: 8–16 weeks
- 60-day post-launch warranty
Embedded Python Squad
A dedicated Python + data + DevOps squad working alongside your team on a continuous release cycle.
- Python + data + DevOps + PM
- Monthly retainer, scale up/down
- Best for: ongoing product roadmap
Honest Answers to Python Reality Questions
The questions every smart buyer asks before signing. Here's what we tell them.
FastAPI or Django?
FastAPI when the product is API-first, async matters, and you don't need an admin / ORM / templating layer for free. Django when you want batteries included — admin, auth, ORM, migrations, RBAC — and the team values productivity over async purity. Both are excellent. We'll tell you which fits your team and your product, not which is cooler this week.
When is Python the wrong choice?
Hard real-time systems. CPU-bound hot paths where you can't drop into C/Rust extensions. Use cases where the JVM, Go, or Rust ecosystem has better tooling for your specific problem. We will tell you honestly when Python isn't the right call, even if it costs us the project.
pandas, Polars, or DuckDB?
Polars when the dataset is bigger than convenient memory and the operations are columnar. DuckDB when the workload looks like SQL and you want zero-ops analytics inside a Python process. pandas when the team already knows it and the data is small enough not to matter. We benchmark the three for your specific workload.
How do you handle ML model serving?
For most teams: a FastAPI service with the model loaded once at startup, batching where it helps, GPU only where it earns its keep. For LLMs at scale: vLLM or TGI behind a thin gateway. For traditional ML: BentoML or a plain FastAPI wrapper. We don't reach for Kubeflow until the team can actually operate it.
How do you do migrations?
Alembic for SQLAlchemy, Django migrations for Django. Versioned, reviewed in PRs, run in CI against a fresh database, never auto-applied in production without a deploy gate. Backward-compatible during the deploy, breaking changes split into expand-then-contract steps.
What about typing?
Type hints everywhere, mypy or pyright in strict mode in CI, Pydantic for runtime validation at the boundaries (HTTP, DB, queues). It's a week of ramp-up and a lifetime of catching bugs in the editor. We don't take untyped Python projects anymore.
How do you handle pipelines and scheduling?
Airflow when the org already runs it, Prefect or Dagster for greenfield with better DX. dbt for warehouse transforms. Pipelines are idempotent, observable per task, retryable, and emit metrics so you know exactly which step failed and how long each takes.
Can you turn our notebook into production?
Yes — that's a lot of what we do. We start with an honest review (is this even the right model / approach), then refactor into a packaged module with tests, types, logging, and a real deployment target. Sometimes the answer is "the notebook is fine, just schedule it" and we'll tell you that too.
Can you take over an existing Python codebase?
Yes. We start with an audit (packaging, dependencies, schema, tests, observability, security), give you a written rescue plan with priorities and effort, then execute incrementally. No rewrites unless the audit genuinely says so — and we'll tell you honestly when it does.
Can you sign an NDA before we share details?
Always. NDA before the first call. Source, schema and data stay under your control. Happy to work inside your tooling and your cloud account if compliance requires it.