About the Role
You will work alongside senior engineers on both client engagements and internal R&D — building business-ready AI applications and agentic systems all the way down to serving infrastructure.
Two things we care about equally: writing solid Python and SQL yourself, and collaborating effectively with AI coding assistants (Claude, GPT, Codex). We treat both as core engineering skills — not one as a substitute for the other. This role is for someone who understands what their code does, can debug at the level of math and memory, and can leverage AI tools to ship faster without sacrificing rigor.
What You'll Do
- Build and prototype AI / GenAI applications — regression, classification, neural networks, fine-tuning.
- Develop GenAI agents — stateless, multi-turn, tool-using — for production use cases.
- Implement text-to-SQL, RAG pipelines, and other LLM-driven workflows on customer data sources.
- Build inference and serving layers with FastAPI and FastMCP; internal UIs in Streamlit.
- Contribute to distributed data and query architectures for AI workloads.
- Use AI cowork tools (Claude Code, Codex) to accelerate delivery — while owning correctness, security, and edge cases.
Required Skills
- DSA — working understanding of fundamentals.
- Machine Learning — core AI/ML concepts; hands-on with NumPy, PyTorch, scikit-learn.
- Generative AI — invoking LLMs, RAG basics, simple agentic workflows; LangChain, Strands, LangGraph.
- System Design — distributed storage and query; intermediate SQL.
- AI Cowork — comfortable using AI-assisted coding agents.
- Interpersonal — clear communication, strong teamwork, eye for detail.
Nice to Have
- Vector databases, embeddings, RAG pipeline experience.
- Cloud platforms — AWS, GCP, or Azure.
- Open-source contributions, side projects, or Kaggle work.
- Prior internship with LLM applications.
Qualifications
- Bachelor's / Master's in CS, AI/ML, Data Science, Mathematics, or related — or equivalent engineering experience.
- 0–2 years of professional or substantial project experience.
- Comfortable in a startup environment — high ownership, ambiguity, fast iteration.
What We Offer
- Real GenAI / ML / data engineering work with paying customers.
- Mentorship from senior engineers (ex-AWS, ex-Google, ex-IBM).
- AI-cowork-first engineering culture.
- Competitive compensation, learning budget, rapid growth path.
- Direct exposure to clients and architecture from day one.
Send your application
Tell us about you, share your GitHub or portfolio, and add a short note (≤ 200 words) on a project where you used an AI assistant well — what you delegated, what you owned, what you would do differently.