Artificial Intelligence Engineer

Sass Innovations; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Sass Innovations in Bengaluru seeks an experienced ML/AI engineer to build and ship production-grade GenAI and agentic AI applications that automate enterprise workflows. This hands-on IC role focuses on delivering end-to-end solutions from design through deployment.

You will work on designing RAG pipelines, deploying on Azure, and implementing guardrails. A strong builder with 5+ years of ML/AI experience and production deployments is required.

Qualifications

• 5+ years ML/AI engineering with production LLM delivery.
• Advanced Python and at least one agent framework.
• Strong LLM and prompt-engineering skills with RAG workflows.
• Azure AI stack and Databricks ML experience.
• Portfolio of 3+ production AI deployments with measurable business impact.

Responsibilities

• Build agentic applications using LangGraph, AutoGen, CrewAI, or Semantic Kernel.
• Design RAG pipelines — chunking, hybrid search, re-ranking, memory, tool orchestration.
• Deploy and monitor AI workloads on Azure (AKS/ARO) with CI/CD and observability.
• Implement responsible-AI guardrails — prompt-injection defense and content filtering.
• Define evaluation metrics for task success, hallucination, latency, and cost.

Skills

ML/AI engineering

Python

Agent frameworks

LLM prompt engineering

Azure AI

Databricks ML

RAG workflows

Production deployments

CI/CD familiarity

Education

B.E./B.Tech or M.Tech in CS/Data Science

Tools

LangGraph

AutoGen

CrewAI

PydanticAI

Azure OpenAI

AI Services

Databricks ML

FAISS

Pinecone

Chroma

Docker

Kubernetes

AKS/ARO

Job description

Experience: 57 years in ML/AI engineering

Reports To: AI Technical Lead / Manager, AIML

Education: B.E./B.Tech or M.Tech in CS, Data Science, or related field

About the Role

Build and ship production-grade GenAI and agentic AI applications that automate enterprise workflows — from design through deployment. A hands-on individual-contributor role for a strong builder.

Key Responsibilities

• Build agentic applications using LangGraph, AutoGen, CrewAI, or Semantic Kernel.
• Design RAG pipelines — chunking, hybrid search, re-ranking, memory, and tool orchestration.
• Deploy and monitor AI workloads on Azure (AKS/ARO) with CI/CD and observability.
• Implement responsible-AI guardrails — prompt-injection defense and content filtering.
• Define evaluation metrics for task success, hallucination, latency, and cost.

Must-Have Skills

• 5+ years ML/AI engineering with production LLM/agentic delivery.
• Advanced Python and at least one agent framework (LangGraph, AutoGen, CrewAI, PydanticAI).
• Strong LLM and prompt-engineering skills (GPT, Claude, LLaMA); hands-on RAG workflows.
• Azure AI stack (Azure OpenAI, AI Search, AI Services) and Databricks ML (MLflow, Delta Lake).
• Vector databases (FAISS, Pinecone, Chroma) and embedding/retrieval design.
• Containerized deployment (Docker/Kubernetes, AKS/ARO) and REST APIs / WebSockets / event-driven services.
• CI/CD and version control (Jenkins / GitHub Actions, Git) with SDLC and agile practices.
• Portfolio of 3+ production AI deployments with measurable business impact.

Nice to Have

• Model fine-tuning (LoRA/PEFT), multi-modal AI, and model evaluation frameworks.
• LLMOps / MLOps and model monitoring (drift, latency, cost, hallucination).
• Knowledge graphs (Neo4j); AWS Bedrock / GCP Vertex AI exposure.
• AI-augmented dev tools (GitHub Copilot, Claude Code, Windsurf) for rapid prototyping.
• Enterprise AI security, compliance, and governance awareness.
• Manufacturing or supply-chain domain experience.