$10K–$60K typical projects
Direct answer
My Python consulting engagements run $10K–$60K: the low end covers a focused audit, performance fix, or small API build, the high end covers multi-month backend and data pipeline delivery. I have 7+ years of production delivery, worked as a Guest Engineer at Expensify, and hold Top Rated status on Upwork with $100K+ earned and verified client reviews. Engagements start with a short diagnostic — I read your code or your architecture before quoting, so the price reflects reality rather than optimism.
Python is where business logic, data, and AI meet, which is why Python problems are rarely just Python problems — they are architecture, data modeling, and deployment problems wearing a Python syntax. Senior consulting means fixing the cause, not the symptom, and leaving your team faster than I found them.
You need an MVP or v2 shipped on budget with someone who makes architecture decisions and owns delivery end-to-end.
You need a senior IC to augment the team, rescue a codebase, or lead mobile + AI integration without months of hiring.
You need a reliable senior subcontractor for client projects — clear communication, store-ready quality, white-label friendly.
30 minutes — goals, stack, timeline, budget range.
Fixed milestones, clear deliverables, start date.
Weekly demos, async Slack updates, production standards.
Store launch, documentation, knowledge transfer.
A product built on Django or FastAPI was fine at a hundred users and is timing out at ten thousand. I profile the real bottlenecks — almost always query patterns and missing indexes before anything exotic — fix the top offenders, add caching where it earns its complexity, and leave a load-test harness so the team sees the next cliff before customers do.
Reports feed on a chain of scripts and cron jobs that fail silently and produce numbers nobody can verify. I rebuild the pipeline with idempotent steps, validation between stages, alerting on failure, and backfill tooling. The outcome is boring in the best way: numbers that reconcile, failures that announce themselves, and an ops runbook a new hire can follow.
A team bolted an LLM feature onto their product and it works, but slowly, expensively, and with no visibility. I restructure the Python service around it — async request handling, response streaming, caching of repeated calls, cost and latency instrumentation per feature — so the AI feature performs like part of the product instead of a bolted-on experiment.
Three patterns cover most of my Python work. Diagnostic engagements, one to two weeks: I read the codebase, profile the system, and deliver a written assessment with a prioritized fix list — useful before a fundraise, after an outage, or when you inherit code from a departed developer. Build engagements, four to twelve weeks: a new API, a data pipeline, an AI service, delivered with tests, deployment, and documentation.
And rescue engagements, the least predictable and most valuable: something in production is failing, slow, or unmaintainable, and the person who built it is gone. Rescues start with stabilization — stop the bleeding, add observability so we can see what the system is doing — before any refactoring, because refactoring a system you cannot observe is gambling. Most clients start with the diagnostic; it is cheap, fast, and makes every subsequent decision better informed.
At the bottom: a diagnostic or a single surgical fix — one slow endpoint, one flaky pipeline — lands $10K–$15K. The middle of the range is bounded builds: a production API with authentication, tests, and deployment, or a data pipeline with proper validation and alerting, typically $20K–$40K depending on integration count and data volume. The top is sustained delivery: multi-month engagements where I own a backend or data platform through several releases.
Two things inflate cost that clients rarely anticipate. Undocumented systems I must reverse-engineer before I can safely change them — reading time is real time. And environments where I cannot get access quickly; every week spent waiting on credentials and VPN tickets is a week of calendar without progress. Two things keep cost down: a reachable person who knows the system's history, and willingness to fix causes rather than paint over symptoms.
First rule: no rewrites by reflex. Existing code that runs in production embodies hundreds of fixed bugs and learned edge cases, and I treat it with respect until there is evidence a piece is beyond repair. I start every change by adding observability — logging, metrics, traces where they are missing — because you cannot responsibly modify what you cannot see. Changes land as small, reviewed pull requests with tests, in your repository, under your CI, from day one.
Second rule: your team levels up as a side effect. I write decision notes explaining not just what changed but why the alternative lost, and I pair with your developers on the tricky parts rather than disappearing into a cave. The engagement fails if you need me forever; it succeeds when the last month of my involvement is mostly your team asking sharper questions.
The most common: hiring on framework keywords instead of systems judgment. Django versus FastAPI is a two-day learning curve for a senior engineer; knowing when a queue beats a cron job, when to denormalize, and when caching will hurt you is a decade. Interview for the judgment. Second: buying a rewrite when you need an audit. Teams in pain assume the codebase is unsalvageable, and consultants happily sell six-month rewrites; in my experience most systems need four weeks of targeted surgery, not a transplant.
Third: accepting work without tests or deployment automation because it was cheaper — you are buying the second engagement at the moment you save on the first. Fourth: no knowledge transfer clause. If nothing in the engagement obliges the consultant to document decisions and pair with your team, you are renting understanding rather than acquiring it, and the meter never stops.
If the problem is a missing product decision — nobody knows what the system should do — a consultant will build you the wrong thing efficiently. Decide first, or hire for discovery explicitly and separately. If the codebase is small and your existing developer just needs a week of focused time, buy them the week; a consultant's ramp-up cost only pays for itself past a certain problem size. If you need someone forty hours a week for a year embedded in team rituals, that is a hire, not a consulting engagement, and pretending otherwise costs a premium for the wrong relationship.
And if your Python problem is actually an infrastructure bill problem — the code is fine but the cloud spend is not — start with a cost review before paying for code changes. Part of a useful diagnostic is telling you which of these categories you are actually in.
You approve milestones and a price before any build starts — no open-ended hourly surprises.
You see running software each week, not status reports, so you can course-correct early.
The person who scopes the work is the person who builds it — no junior layers, no agency markup.
Top Rated on Upwork with public client reviews and $100K+ earned, plus contributions to Expensify. Check the receipts before you commit.
Chat-Based Financial Collaboration
Contributed 50+ merged pull requests to New Expensify — the production React Native app used by millions for chat-based expense management — working through Expensify's fully public open-source process alongside their core engineers, and recognized with a Guest Engineer invitation to their Curaçao offsite.
Food Truck Discovery Platform
Production food-truck discovery platform for US cities — customer and vendor React Native apps on iOS and Android, plus customer and vendor web, with AI features. Helps people find trucks nearby while vendors manage schedules, orders, and events.
Rewards & Engagement Platform
End-to-end React Native app for the US market with gamified rewards, real-time engagement, and secure payment flows — built from product scoping to App Store launch.
Senior Python consultants typically bill $80–$180 per hour. On a project basis: a codebase audit or single performance fix runs $10K–$15K, a bounded build such as a production API or data pipeline runs $20K–$40K, and sustained multi-month backend ownership reaches $60K. A short paid diagnostic first is the best way to get a fixed quote grounded in your actual code.
Diagnostics take one to two weeks and end with a written, prioritized findings report. Focused builds and performance rescues run four to twelve weeks. Longer engagements exist but should taper by design — a good consultant transfers knowledge to your team as they go, so the final month is mostly handover rather than dependence. Be wary of open-ended engagements with no defined end state.
Hire a consultant for bounded, senior-shaped problems: an audit, a rescue, a build your team lacks the specialty for, or an architecture decision you will live with for years. Hire full-time when there is a standing stream of Python work measured in years. Consultants cost more per hour and less per problem; the mistake is using one as a long-term staff substitute at consulting rates.
Projects typically fall in the $10K–$60K range depending on scope, integrations, and timeline. I provide a fixed-scope proposal after a 30-minute scoping call.
MVPs often ship in 8–12 weeks. Production systems with AI backends or RAG may run 12–20 weeks. Rescue and audit engagements can start within days.
Yes. I work with founders, CTOs, product teams, and agencies worldwide — US, UK, EU, and APAC time zones with async updates and weekly demos.
Yes. I specialize in React Native + Python (FastAPI) + AI (RAG, agents, OpenAI/Claude) under one senior owner — fewer handoffs, faster shipping.
Book a free 30-minute scoping call on this site, hire through Upwork, or email dhairyasenjaliya@gmail.com with your brief and timeline.
30-minute scoping call · Clear milestones · Senior engineer ownership