Job Description & Details
This is a heavy-hitting hands-on engineering gig bridging local business stakeholders with an offshore delivery team. You'll be building enterprise GenAI apps, RAG solutions, and AI agents from scratch while wrangling everything from Python backends to React frontends. If you like being the technical anchor who actually talks to the business, prototypes rapidly, and pushes features to production, this 12-month contract is worth your time.
What You'll Actually Be Doing
Expect to spend your days knee-deep in Python prototyping, standing up RAG pipelines, and wiring up enterprise assistants. You won't just write isolated code; you'll figure out vague requirements from local stakeholders, build quick proof-of-concepts to win them over, and then hand off clear, actionable tasks to the offshore engineering and QA squads. Document ingestion, vector database integration, and ironing out conversational workflow hiccups will be part of your daily bread.
The Core Tech Stack
Python is non-negotiable here—you need to live and breathe it for backend services, APIs, and data ingestion pipelines. On top of that, deep familiarity with GenAI ecosystems, LLM APIs, prompt engineering, and vector databases is mandatory because you'll be building retrieval-augmented generation systems. Don't sleep on your frontend skills either, as you'll need to spin up UIs using React and TypeScript to demo your AI agents directly to stakeholders.
Interview Expectations
When you sit down with the hiring team, expect them to grill you on how you handle latency and context window limits in production-grade RAG applications. They are secretly testing whether you can build resilient fallback mechanisms when an LLM hallucinates or times out. You should also be ready to walk through a time you translated messy business requirements into clean microservices architecture while coordinating across a distributed team.
Application Advice
To get past the ATS and grab the recruiter's attention, make sure your resume explicitly highlights end-to-end GenAI implementations rather than just traditional web apps. Mirror the exact keywords from the job description—terms like vector databases, embedding pipelines, RAG solutions, and API integrations need to jump off the page. Emphasize your experience working directly with stakeholders and offshore teams, as they need a proven communicator, not just a code machine.