Job Title: Senior AI Engineer (Agentic Systems & Data)

Role Overview

We are looking for a Senior AI Engineer to lead the development of our next-generation Generative AI platform. You will be responsible for building complex, stateful AI agents using LangGraph and deploying them via AWS AgentCore. This role sits at the intersection of our data lakehouse in Databricks and our AI services on AWS, requiring a candidate who can bridge the gap between large-scale data retrieval and autonomous agent reasoning.

Key Responsibilities

- Agentic Orchestration: Design and implement multi-agent workflows using LangGraph, focusing on cycles, persistence, and complex state management.
- AWS AgentCore Integration: Deploy and manage production-ready agents using AgentCore Runtime, leveraging its capabilities for session isolation, memory persistence, and secure tool access via AgentCore Gateway.
- Data Integration: Architect seamless data connectors between Databricks (Delta Lake) and AWS-native AI services. You will ensure that our agents have low-latency access to governed data without relying on Databricks Genie.
- Tool & MCP Development: Build and expose internal APIs and Databricks functions as tools for AI agents using the Model Context Protocol (MCP) and AgentCore Gateway.




- Scalable Deployment: Manage the end-to-end lifecycle of LLM applications, including versioning, automated scaling in AWS, and "Human-in-the-loop" (HITL) workflow implementation.
- Observability & Evaluation: Use AgentCore Evaluations to monitor agent behavior, correctness, and tool-selection accuracy in production.

Required Technical Skills

- Frameworks: Mastery of LangChain/LangGraph (specifically stateful graphs and checkpointers).
- AWS Ecosystem: Solid experience with Amazon Bedrock and AgentCore (Runtime, Memory, Identity, and Gateway).
- Data Platforms: Proficiency in Databricks, including Spark SQL, PySpark, and Unity Catalog for data governance.
- Language: Expert-level Python (async programming, Pydantic, and API development).
- Cloud Infrastructure: Hands-on experience with AWS IAM, Lambda, and ECR for containerized agent deployment.

Preferred Qualifications

- Experience with Cedar or AWS Policy for defining agentic boundaries.
- Familiarity with Vector Databases (e.g., Pinecone, Weaviate, or OpenSearch) for RAG-enhanced agents.
- Background in building custom "Code Interpreter" or "Browser" tools for autonomous agents.

📌 AI Engineer (India)
🏢 Cloudmetica Technologies
📍 India