Artificial Intelligence Engineer

Implement AI applications using Model Context Protocol (MCP) for secure and standardized tool/model interaction. Integrate foundation models from Amazon Bedrock and third-party providers into enterprise applications. Design and optimize prompt engineering strategies, agent memory, and context management.

Build APIs and microservices to expose AI functionalities to business applications. Collaborate with Data Engineering teams to develop data pipelines supporting AI use cases. Implement monitoring, logging, governance, and security controls for AI systems.

Mentor junior AI engineers and contribute to AI engineering standards and best practices. Evaluate emerging AI technologies and recommend improvements to the AI platform ecosystem. Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

5 years of experience in AI/ML engineering and production-grade AI solution development. Strong hands-on experience with AWS AI/ML ecosystem, including: Amazon Bedrock, SageMaker, Lambda, API Gateway, ECS/EKS, Step Functions, DynamoDB / RDS / S3 Experience building Agentic AI systems using:Bedrock Agents / AgentCore, Multi-agent orchestration frameworks, Tool calling and workflow automation Strong understanding and implementation experience with: MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), Vector databases, Embedding models, Prompt engineering Proficiency in Python and AI frameworks such as: LangChain, LangGraph, LlamaIndex, FastAPI, PyTorch / TensorFlow (preferred) Experience deploying scalable AI applications using Docker and Kubernetes. Strong understanding of software engineering best practices, CI/CD, and MLOps.

Experience working with structured and unstructured data processing pipelines. Strong analytical, troubleshooting, and communication skills To adhere to the Information Security Management policies and procedures.