GenAI Engineer

Job Overview

Key Responsibilities

Design, build, and iterate on GenAI-powered applications and agents using Python

Rapidly prototype AI solutions for real-world use cases (banking, enterprise ops, analytics, etc.)

Work across the stack backend logic, APIs, integrations, and basic frontend components when needed .

Integrate LLMs, embeddings, vector databases, and external tools into cohesive workflows.

Translate ambiguous problem statements into working demos or production-ready components.

Build reusable abstractions, templates, and utilities that accelerate future development.

Collaborate closely with business analysts, solution architects, and product teams.

Continuously evaluate new developments in the GenAI ecosystem and apply them pragmatically.

Required Skills

Strong proficiency in Python (core language, data structures, APIs) Hands-on experience with GenAI / LLM-based systems (OpenAI, Anthropic, open-source models, etc.).

Understanding of prompting, embeddings, RAG, agents, and tool calling.

Full-stack exposure: *Backend frameworks (FastAPI, Flask, Django, or similar) *Frontend basics (React, Angular, or modern JS frameworks depth not mandatory).

Experience integrating third-party APIs, databases, and cloud services.

Solid computer science fundamentals (data structures, algorithms, system design basics)

Nice to Have

Experience with vector databases (FAISS, Pinecone, Weaviate, OpenSearch, etc.).

Familiarity with cloud platforms (AWS preferred).

Exposure to enterprise data systems (SQL, analytics, internal tools)

Prior work on demos, hackathons, accelerators, or fast-paced product teams.

Curiosity for AI systems beyond just calling an API

What are Really Looking For

Someone who learns extremely fast and is comfortable with ambiguity.

A strong problem-solver who can go from idea working solution quickly.

Engineers who enjoy building things, not just discussing architecture

High ownership mindset you see a problem and instinctively try to solve it

A bias toward action, experimentation, and continuous improvement