AI Engineer - Python/Machine Learning

What You'll Work On:

1. Generative AI Core Engineering & Agent Orchestration:

• Help architect and deploy multi-agent workflows using modern orchestration frameworks (LangGraph, LlamaIndex, Bedrock, Vertex AI) to solve complex, multi-step business problems.
• Design and build Retrieval-Augmented Generation (RAG) systems, fine-tuned LLMs, high-performance embeddings pipelines, and document intelligence solutions.
• Contribute to productionizing LLM-based agents for claims validation, pre-authorization, sales advisory, care management, and internal AI copilots.
• Support work on advanced models, including transformers, diffusion models, and multi-modal (vision, speech) models for specialized health-tech challenges.

2. Full-Stack Product Engineering:

• Work across the entire UI - API - Model workflow, ensuring seamless, high-performance integration from front-end to inference.
• Build user interfaces using modern frameworks (Next.js, React, Streamlit) for both customer-facing and internal operational tools.
• Develop scalable backend microservices (Python/FastAPI, Node.js/Express) optimized for LLM inference and large-scale data serving.

3. LLMOps & Deployment:

• Ship models to production using modern MLOps tooling (MLflow, CI/CD, Docker, Kubernetes).
• Help optimize latency, throughput, inference cost, and security for deployed GenAI services.
• Build monitoring and observability for GenAI: model drift, hallucination and toxicity rates, guardrail/compliance violations, and agent decision tracing.

Eligibility:

• Graduation year: Open to 2025 graduates from IIT(Delhi/Mumbai/Chennai/Kanpur) only.
• Degree: B.Tech / M.Tech / M.S. / MCA or equivalent
• Academics: Strong, consistent academic record across qualifying examinations.

What You Bring to the Table:

Must-Haves:

• Strong fundamentals in algorithms, data structures, distributed systems, and applied Machine Learning / Deep Learning.
• Hands-on exposure to LLMs and at least one agent/orchestration framework (LangChain/LangGraph, LlamaIndex, Bedrock, or Vertex) through projects, internships, or research.
• Working proficiency in Python; familiarity with a modern web stack (React/Next.js front-end, FastAPI or Node.js back-end).
• A builder's mindset - comfortable in ambiguous, "0 to 1" environments where you help define both the product and the architecture.

Preferred:

• Experience with multi-modal models (vision, speech) relevant to healthcare/insurance documentation.
• Exposure to fine-tuning LLMs or training custom small models for domain-specific tasks.
• Interest in or exposure to healthcare, insurance, or financial services domains.
• Familiarity with optimizing LLM inference (quantization, compilation, specialized)