Vetted engineers with verified commercial track record in Python
Khmelnytskyi, Ukraine
Full-Stack Developer, Fractional CTO — Websites, Apps, Servers, Databases, AI, SEO & QA
Full-stack developer working across the entire stack — websites, apps, servers, databases, AI integrations, SEO, and QA. Languages & Core: writes code in JavaScript, TypeScript, Python, PHP, Go, and Rust. Architects scalable systems for large-scale projects. Frontend & Interfaces: builds websites and web applications with React and Next.js. Crafts responsive interfaces with Tailwind CSS, Radix UI and Shadcn, adds smooth animations with Framer Motion, and interactive charts with Recharts. SEO Audit & On-Page Optimization: semantic keyword research, resolving technical indexing issues, and optimizing page load speeds. Structures page architecture, meta tags, and multi-language support (i18n). Copywriting & Content Strategy: writes technical articles, drafts precise content briefs for writers, and develops content plans. QA & Testing: full-cycle testing for websites, web services, and Android apps — manual QA for UI/UX and business logic, plus automated testing with Jest, Vitest, Playwright and E2E. Backend, Cloud & Databases: complex API integrations of any scale. Builds servers with Node.js (Express, Fastify). Works with PostgreSQL, MySQL and MongoDB, ORMs (Prisma, Drizzle), and cloud infrastructure (Supabase, Firebase, Cloudflare). Browser Extensions & Automation: develops Manifest V3 browser extensions for Chrome, Edge, Firefox and other browsers. Builds web scrapers for complex data extraction using Puppeteer and Playwright. AI & Intelligent Agents: builds custom AI agents and integrates LLMs from OpenAI, Google Gemini, and Anthropic Claude via API, including Claude Code setups. Servers & DevOps: Linux (Ubuntu) and VPS administration — setup, updates, real-time monitoring, secure process isolation, Nginx, PM2, and CI/CD deployment via GitHub Actions. Telegram Bots: develops advanced Telegram bots (Telegraf, Grammy) integrated with AI, payment gateways, Google Sheets, and crypto exchanges. Desktop Applications: builds cross-platform software for Windows and macOS using Electron and Rust. Additional expertise: site development and customization with WordPress and Astro.
Khmelnytskyi, Ukraine
Senior Backend Engineer | AI Engineering | PHP, Python, LLM Applications
Senior backend engineer working across PHP and Python, with a growing focus on AI engineering: building LLM-powered application features on top of existing backend systems and APIs.
CTO & Founder at Punch AI | Building Software Harnesses for AI-Powered Development | PhD Student in Software Engineering
CTO and Founder at Punch AI, building software harnesses that make AI-powered development safer and more reliable. PhD student in Software Engineering, bridging applied AI engineering with academic research.
AI/LLM Engineer | RAG & Agent Orchestration | LangChain/LangGraph | Python | Building AI Content Platform (1,500+ users)
AI/LLM engineer specializing in RAG pipelines and multi-agent orchestration with LangChain and LangGraph in Python. Currently building an AI content platform that has grown to over 1,500 users.
London, United Kingdom (London, United Kingdom · Hybrid)
Senior Rust Engineer | HPC | Equity Derivatives at HSBC
Senior Software Engineer with 18+ years of experience designing innovative distributed data- and event-streaming systems in FinTech, security, and analytics. Specializes in Rust for high-performance computing (HPC) and equity derivatives at HSBC. Previously at Nillion Labs built network stacks and decentralized confidential data computation on Rust.
London, United Kingdom (London Area, United Kingdom)
Rust Engineer | High-Frequency & Algorithmic Trading Systems
Rust engineer with a background in high-speed analytics, databases, and algorithmic trading. Worked as a Rust engineer at Keyrock — a leading European digital-asset market-making and algo-trading firm. Specializes in parallel market-data processing, low-level ETL pipelines, hardware-interface integration, and high-performance AWS-based backends.
Gliwice, Silesian Voivodeship, Poland (Gliwice, Poland)
IT Systems Administrator and self-employed infrastructure specialist (Linux | Windows Server | VMware | Cisco | Zabbix)
Independent Polish system administrator and infrastructure specialist. Provides audit, deployment, and support services for Linux and Windows Server servers, MSSQL database configuration, VMware ESXi virtualization, Cisco and FortiGate networking solutions. Handles regular backups (NAS/FreeNAS), implements Zabbix monitoring systems, and automates daily tasks with Python.
London, United Kingdom (London, United Kingdom)
Lead AI Engineer at The Evidence Company | Agentic AI, LLM Systems, Production RAG & Knowledge Graphs
Lead AI engineer with over 10 years of experience in machine learning, data engineering, and agentic systems. Leads the technical direction and AI platform architecture at an Imperial College London spin-off (The Evidence Company). Designed from scratch an agentic knowledge-graph and RAG system for translating and analyzing large texts with Neo4j, LangChain, and vector embeddings. At Builder.ai, automated requirements-based documentation generation with NLP/LLM for 500+ enterprise projects. Author of open-source AI projects (MoM LLM for parallel multi-model orchestration, learners-mcp built on Model Context Protocol). Specializes in quality evaluation (evals), schema validation, hallucination guardrails, and optimizing LLM call costs.
London, United Kingdom (London, United Kingdom)
Senior Research Infrastructure Engineer at Imperial College London | Linux | HPC | Ansible | Slurm
Lead server and HPC infrastructure engineer with 10+ years of experience in Linux systems administration, automation, network and storage configuration. Manages a large-scale Linux server platform at Imperial College London: RHEL 8/9 administration on bare-metal servers and VMware virtualization (clusters with 1,000+ nodes), maintenance of GPFS / IBM Spectrum Scale parallel filesystems (500+ systems), Mellanox InfiniBand / RDMA networking, Slurm and PBS workload schedulers. Automates configurations with Ansible, Terraform, and GitLab CI/CD, provides monitoring via Grafana and InfluxDB.
London, England, United Kingdom (London, United Kingdom)
Co-Founder & CTO @ BabelBond | AWS Certified Solutions Architect | Ex-Head of Tech @ Reckon Digital
Certified AWS Solutions Architect and CTO with 10+ years of hands-on experience designing cloud backends and microservices platforms. As Head of Technology at Reckon Digital, responsible for designing and deploying production systems on AWS for the UN (World Food Programme) and international enterprises (Baker Hughes). Specializes in serverless and containerized services on AWS (Lambda, ECS, CDK, CloudFormation), highly reliable relational databases (RDS Aurora), and data security.
Rottenburg / Tübingen, Baden-Württemberg, Germany (Germany)
Linux System Administrator | HPC Cluster Administrator at Tübingen AI Center
Linux infrastructure and cluster system administrator. Specializes in designing, deploying, and maintaining virtual environments on Proxmox VE, building fault-tolerant Ceph storage, automating configurations via Ansible and AWX, and managing infrastructure as code (Terraform). Ensures stability of bare-metal server nodes, network equipment (VLAN, pfSense, MikroTik), and monitoring systems.
London, England, United Kingdom (London, United Kingdom)
Senior Software Engineer at On | Full-Stack & Backend Systems Architecture
Senior Full-Stack engineer and consultant with 8+ years of commercial experience designing and scaling high-load digital platforms. Lead engineer at Swiss-British sports brand On, responsible for designing and scaling the customer profile platform. Deep expertise in TypeScript, React, and Node.js microservice architecture, technical interviewing, and adopting AI tooling in development.
London, England, United Kingdom (London Area, United Kingdom · Hybrid)
Solutions Engineer @ AWS (Prototyping & Customer Engineering)
Solutions engineer on the Amazon Web Services PACE (Prototyping and Customer Engineering) team. Helps the largest global AWS customers turn complex business challenges into working production solutions. Holds 10 top-level AWS certifications (Professional and Specialty), including Solutions Architect Professional, DevOps Engineer Professional, Generative AI Developer Professional, and Machine Learning Specialty. Specializes in GenAI integration (Amazon Bedrock, AgentCore, MCP), SageMaker, next-generation serverless databases (Aurora DSQL), and building cloud architectures for e-commerce and enterprise.
London, United Kingdom (London Area, United Kingdom · Hybrid)
Forward Deployed Engineer @ Stripe | Developer, Visiting Instructor, Mentor
Stripe team engineer (Forward Deployed Engineer at Stripe). Works directly with key business clients on architecture and delivery of complex payment solutions: Stripe Connect marketplaces, multi-currency billing, complex payment pipelines. Deep background in full-stack development (React, Next.js, Node.js, Python, GraphQL).
London, United Kingdom (London, United Kingdom)
Solutions Architect Healthcare & Life Sciences @ Amazon Web Services (AWS)
Cloud solutions architect at Amazon Web Services, specializing in Healthcare & Life Sciences. Builds secure, compliant cloud environments on AWS, delivering solutions based on generative AI (Amazon Bedrock) and serverless microservices for sensitive medical data.
Khmelnytskyi, Ukraine
Director at Print Word LLC | LLM & Content Automation
Director at Print Word LLC, applying LLM-based content automation to printing and publishing workflows: order processing, document generation and customer communication.
Full Stack Developer | Backend Developer | Software Developer | AI Developer
Full stack developer covering backend services and frontend delivery, with growing specialization in integrating AI features such as classification and retrieval into product workflows.
London, United Kingdom (London Area, United Kingdom)
Senior Full Stack Engineer at Marcura | PostgreSQL & Python/Node.js Specialist
Senior Software Engineer with extensive experience optimizing and scaling relational databases under high loads. At Marcura and ARTERA, responsible for designing data-storage structures in PostgreSQL and MySQL, removing query bottlenecks, configuring connection pools, and scaling cloud database instances on AWS RDS.
London, United Kingdom (London, United Kingdom · Hybrid)
Software Engineer | Imperial College | Python & Rust at Evervault
Software engineer at Evervault — a leading service for payment-data protection, encryption, and confidential computing. Graduate of Imperial College London. Specializes in Rust, Python, and TypeScript for building fast, reliable, resilient card-encryption systems and transaction-routing APIs. Extensive experience architecting secure cloud services on AWS.
London, England, United Kingdom (London, United Kingdom)
Python · FastAPI · React · Supabase · PostgreSQL · Gemini API · Docker | AI / Full-Stack Engineer
Full-stack and AI engineer with a master's degree from Queen Mary University of London. Builds and launches production services from scratch: designed and released the pdfexy.com platform on FastAPI, React, Supabase (PostgreSQL), and Vercel. At Varua Transport built a scalable fleet-management system with a REST API, an LLM interface for natural-language queries, and real-time dashboards connected to Supabase.
London, United Kingdom (London, United Kingdom)
Software Engineer (AI & Full Stack) | React · Node.js · FastAPI · Python | LangChain · RAG · Agents | PyData London & LangChain London Speaker
Full-stack AI engineer with a master's degree in Computer Science (Machine Learning & AI specialization, GPA 4.0). Builds the full AI product stack: from React frontends and FastAPI/Node.js backends to RAG pipelines and production multi-agent systems. Worked as an engineer at DXC Technology and Alankrita, designing agent orchestration architecture, evaluation (evals) frameworks, and context engineering. Regular speaker at the PyData London and LangChain London communities on production AI agent reliability.
London, England, United Kingdom (London, United Kingdom)
AI Engineer | Forward Deployed Engineer | RAG Systems · AI Agents · LLMOps | Python · LangGraph · AWS | Ex-ThoughtWorks
AI engineer with a background in building highly reliable distributed systems (4+ years at ThoughtWorks). Specializes in building agentic systems with LangGraph, RAG pipelines, and serverless AI infrastructure on AWS Bedrock (Claude Haiku/Sonnet). At Elara, built an LLM-based enterprise communications analytics platform, processing over 94K messages through a 6-stage GenAI pipeline with structured JSON parsing and statistical normalization.
Python powers backend services, automation pipelines, and machine learning infrastructure worldwide. IT consulting agencies routinely bill $150 to $250 per hour for Python development, adding non-technical account management layers that slow down delivery schedules.
ProofDevs connects you directly with independent senior Python developers who write clean, type-hinted code, profile asynchronous event loops, and deploy production services at transparent rates between $45 and $95 per hour.
| Staffing Metric | Traditional Staffing Agency | ProofDevs Independent Senior |
|---|---|---|
| Hourly Billing Rate | $150 – $250 / hr | $45 – $95 / hr |
| Technical Vetting | Keyword resume scanning by recruiters | Code audits and production track-record verification |
| Contract Flexibility | Rigid multi-month contracts with minimums | Zero retainers: book by exact task or weekly sprint |
| Direct Communication | Filtered through account executives | Direct technical collaboration with the engineer |
| IP & Code Ownership | Agency contracts with proprietary clauses | 100% client repository and credential ownership |
Python's simplicity allows rapid prototyping, but scaling production services requires deep knowledge of asynchronous I/O (`asyncio`), connection pooling, and memory management. In Django and SQLAlchemy applications, unoptimized ORM queries cause severe latency spikes under concurrent loads.
Senior Python engineers resolve these bottlenecks by optimizing SQL queries, implementing asynchronous request handlers in FastAPI, and offloading compute-heavy tasks to distributed background workers using Celery or Redis Queues (RQ).
For data ingestion pipelines, developers leverage streaming generators, multiprocessing pools, and efficient data serialization (Pydantic v2 with Rust core) to process millions of records without triggering out-of-memory errors.
Modern Python development demands strict typing via `mypy` or `pyright`, dependency management with `uv` or `poetry`, and comprehensive test suites.
Architecture Rule: The GIL and Asynchronous I/O
The Python Global Interpreter Lock (GIL) prevents multi-threaded CPU-bound parallelism. Senior Python engineers handle I/O-bound operations with asynchronous event loops and delegate CPU-bound computations to separate worker processes, avoiding server event loop starvation.
Timelines for common commercial Python tasks are structured below.
Independent Python engineers charge between $45 and $95 per hour. You retain 100% intellectual property ownership of all Git repositories, test suites, Docker containers, and environment credentials upon milestone approval.
Pick Django 5.x when you need admin panels, ORM migrations, and authentication out of the box for content-heavy products or internal tools. A booking platform or marketplace MVP reaches production in 40 to 80 hours because django-allauth, Django REST framework, and Celery 5.4 beat writing auth, pagination, and background jobs by hand. Teams of two to five engineers stay productive since conventions for settings, apps, and migrations are shared.
Choose FastAPI 0.115 with Pydantic v2 and Uvicorn when request throughput and typed contracts matter, for example webhook receivers handling 2,000 to 8,000 requests per minute or typed SDK backends for React clients. Async endpoints backed by asyncpg and Redis cut p95 latency to 40 to 120 ms in typical CRUD workloads. Expect 25 to 60 hours for a typed service with pytest, ruff, mypy, and Docker Compose wired from day one.
Keep Flask 3.x for small glue services and keep ETL workers outside request servers with Celery, RQ, Dramatiq, or Prefect plus Polars for frame processing. Flask suits a 10 to 25 hour admin webhook or prototype, while workers with Postgres LISTEN/NOTIFY or RabbitMQ isolate retries and dead-letter queues. If CPU work dominates, split GIL-bound code into separate containers and keep the API event loop free for I/O.
| Option | Build speed | Runtime and upkeep | Pick when |
|---|---|---|---|
| Django 5.x + DRF + Celery | 40-80 hrs for MVP | $45-$95/hr; admin and auth included | Marketplaces, SaaS admin, content products |
| FastAPI + Pydantic v2 + Uvicorn | 25-60 hrs for typed service | 40-120 ms p95 on CRUD; async I/O | Webhooks, partner APIs, high-request services |
| Flask 3.x minimal service | 10-25 hrs for glue service | Low upkeep for single-purpose endpoints | Prototypes, admin hooks, internal tools |
| Celery/RQ workers + Polars | 15-40 hrs for pipeline | Isolated retries, dead-letter queues | ETL, billing reconciliation, scheduled jobs |
Legacy Python estates usually stall on Python 2.7 or 3.8, Django 1.11 to 2.2, pinned Celery 4, and untyped code without pytest coverage. Engineers start with a 6 to 10 hour audit: dependency tree via pipdeptree, CVE list from pip-audit, failing import inventory, and SQL logs showing N+1 hotspots. The strangler pattern then routes one endpoint or worker at a time behind an Nginx or Traefik flag instead of freezing releases for a rewrite.
Phase one moves packaging and quality gates: uv or pip-tools lockfiles, ruff and black formatting, mypy in non-blocking mode, and pytest with factory_boy fixtures running in GitHub Actions. Phase two upgrades interpreters and frameworks incrementally, for example 3.8 to 3.12 in a 15 to 30 hour band, then Django point releases with migration dry runs on a staging clone. Each slice ships behind feature flags with rollback notes and Sentry error budgets.
Data work runs in parallel: pg_dump rehearsals, backfill scripts with batched UPDATE LIMIT 1000, and dual-write checks comparing old and new query paths for 48 hours. Typical bands are 20 to 45 hours for packaging plus CI repair, 30 to 70 hours for Django and Celery upgrades with regression tests, and 15 to 35 hours for worker and cron cutover with Flower and Prometheus monitoring attached.
Ask candidates to audit the repository before quoting a build. Provide GitHub access, pyproject.toml or requirements files, Dockerfile and Compose setup, README runbook, recent Sentry traces, and a sanitized Postgres dump or schema.sql with row counts. A senior engineer returns a 4 to 8 hour report covering dependency CVEs, dead code, test gaps, query hotspots from django-debug-toolbar or pg_stat_statements, and Celery retry storms.
Watch how they separate fixable debt from structural failure. Fixable signs include missing type hints, flaky fixtures, slow admin pages, and unindexed foreign keys that resolve in 15 to 40 hours. Rewrite signals are different: vendored interpreters nobody can install, circular Django app imports, monetary logic without tests, or background jobs that lose payloads on restart. Seniors name the threshold in hours instead of promising to salvage everything.
Rewrite threshold
Recommend a rewrite when installs are unrepeatable, billing paths lack tests, or jobs drop payloads on restart. Those cases usually cost 80 to 160 hours to rebuild against 60 to 120 hours of patching with outage risk left over. Anything below that line — typing, fixtures, indexes, queue retries — is a fix engagement, not a rebuild.
Screen for production habits, not syntax trivia. Seniors describe GIL trade-offs, asyncio backpressure, Postgres index choices, and Celery idempotency keys from incidents they handled. Juniors recite decorators and list comprehensions but stall on connection pool sizing, N+1 traces, or Docker layer caching. Ask for a merged pull request that cut latency or memory, plus a postmortem where their flag or migration caused an outage.
Run a paid 3 to 5 hour trial against your code: fix one N+1 endpoint, add pytest coverage above 80 percent for a billing module, and containerize it with health checks. Acceptance criteria are concrete: p95 latency under 300 ms on seeded data, zero mypy errors in touched files, ruff clean, and a README section explaining queue retries. Pay $45 to $95 per hour for the trial and hire when the diff is reviewable without follow-up cleanup.
You will receive matches with available senior Python engineers within 2 to 4 hours of submitting your technical requirements.
Rates range from $45 to $95 per hour, set directly by each verified engineer based on framework specialization in FastAPI, Django, or data pipelines.
Yes. Senior engineers profile query execution plans, eliminate N+1 bottlenecks, and configure connection pools to reduce API latency.
Yes. Profiles specify experience in FastAPI, asyncio, Celery, and high-concurrency event-driven architectures.
You own 100% of all Git repositories, Dockerfiles, and deployment assets upon milestone completion.
Pick Django 5.x for admin-heavy products needing auth and ORM migrations in 40 to 80 hours; pick FastAPI 0.115 with Pydantic v2 for typed high-request APIs in 25 to 60 hours. Flask 3.x fits 10 to 25 hour glue services. Share your request volume and admin needs and the match favors the stack with the lower upkeep.
A typed FastAPI service takes 25 to 60 hours, a Django MVP 40 to 80 hours, and an ETL pipeline 15 to 40 hours. Query tuning lands in 8 to 20 hours and webhook integrations in 10 to 22 hours. You get a fixed hour band before work starts at $45 to $95 per hour.
Yes, if you provide repo access, lockfiles, Docker setup, Sentry traces, and a sanitized DB schema. A 4 to 8 hour audit maps CVEs, N+1 queries, and Celery retry faults. Rewrite is advised only when installs are unrepeatable or billing logic lacks tests.
Run a paid 3 to 5 hour trial: fix one N+1 endpoint and add pytest coverage for a billing module. Hire when p95 latency drops under 300 ms with mypy and ruff clean. Seniors show pool sizing and rollback plans; juniors stall on EXPLAIN output.
Provide GitHub access, pyproject.toml or requirements files, .env.example, Docker Compose files, a staging database snapshot, and Sentry or log access. Missing secrets or schema dumps add 4 to 8 hours of discovery billed at the engineer's $45 to $95 hourly rate.
Yes. Most offer a 2 to 4 week bugfix window at $45 to $95 per hour covering regressions, worker failures, and migration rollbacks. You keep runbooks, dashboards in Grafana or Datadog, and alert rules, so on-call handoff takes 2 to 3 hours.
Independents charge $45 to $95 per hour versus $150 to $250 per hour at agencies. Matching takes 2 to 4 hours and you pay per sprint with zero retainer. A 40-hour build saves roughly $4,000 to $6,000 against agency billing.
