We are seeking a hands-on Senior AI Engineer to design and build enterprise-grade Agentic AI applications within the AWS ecosystem.

The ideal candidate should have strong Python development experience, practical exposure to Large Language Models (LLMs), and hands-on experience building Retrieval-Augmented Generation (RAG) and multi-agent workflows using frameworks such as LangChain/LangGraph or similar.

This role focuses on designing, integrating, deploying, and operating AI-powered services on AWS, ensuring scalability, security, reliability, and cost efficiency.

Key Responsibilities:

- Design and implement Agentic AI workflows using LLMs (OpenAI, Bedrock, or equivalent).

- Develop Python-based backend services for: Model orchestration, Tool/function calling, Context management, API exposure.

- Build Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., Pinecone, FAISS, OpenSearch).

- Design secure and scalable AI APIs using AWS services such as Lambda and API Gateway.

- Deploy and manage AI workloads using AWS Bedrock and SageMaker.

- Implement CI/CD pipelines and Infrastructure as Code (CloudFormation/CDK/Serverless Framework).

- Apply MLOps best practices including: Model versioning, Prompt versioning, Monitoring and observability, Logging and performance tracking.

- Ensure AI systems are: Reliable and resilient, Secure (IAM, policies, encryption), Cost-aware and optimized.

- Collaborate with product owners, enterprise application teams, and data engineers to integrate AI solutions into business workflows.

- Contribute to AI use case evaluation and architecture design discussions.

Requirements:

- 57 years of software engineering experience with strong Python expertise.

- Hands-on experience integrating LLM platforms such as OpenAI or AWS Bedrock.

- Practical experience building: RAG systems, Multi-agent or agentic workflows, Tool-calling or function-calling architectures.

- Experience with frameworks such as: LangChain / LangGraph (or equivalent orchestration frameworks).

- Solid experience with AWS services: Bedrock, SageMaker (model hosting/inference; full training expertise not mandatory), Lambda, ECS, S3, API Gateway, IAM etc.

- Experience deploying applications using Infrastructure as Code (CloudFormation, CDK, Serverless Framework).

- Understanding of: Prompt engineering, LLM limitations and guardrails, Context window management, Token optimization.

- Familiarity with vector databases and embedding workflows.

- Strong debugging, problem-solving, and system design skills.

Preferred:

- Experience designing secure enterprise AI solutions.

- Exposure to SAP AI, enterprise ERP integration, or business process automation.

- Experience implementing observability for AI systems (latency, hallucination tracking, usage monitoring).

- Experience with TypeScript for AI tooling or frontend integrations