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# Senior AI/ML Engineer

- **Company:** Tata Consultancy Services Limited
- **Location:** Mason, OH, United States
- **Experience:** Expert
- **Salary:** $95,000.0 - $145,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Amazon Web Services, Amazon S3, Microsoft Azure, Cloud Engineering, Continuous Integration, Relational Databases, Software Debugging, Distributed Systems, Python (Programming Language), PostgreSQL, Machine Learning, Language Modeling, Object-Oriented Software Development, Systems Development Life Cycle, OpenAI, Software Engineering, Systems Integration, Data Logging, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, AgentCore, Agentic-AI, Pytest, Containerization, Kubernetes, Information Technology, Amazon Bedrock, Hashicorp, Virtual Agents, Asynchronous Programming, Evaluation of Large Language Models, Api Design, Cloudwatch, Restful APIs, Model Context Protocol, Network Server, Dynatrace, Docker, Agent2Agent Protocol
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-ai-ml-engineer-mason-oh--7370d2ac-0483-45c2-8aa5-d0171f5a9324

## About the Role

Artificial Intelligence (AI), Computer Science, Software Engineering, Artificial Intelligence (AI), Software Design, Modeling Languages, Cloud Architecture, Artificial Intelligence (AI) Agents, Scalable System Development, Cross-Functional, Required Skills: Artificial Intelligence | Machine Learning

Qualification: BACHELOR OF COMPUTER SCIENCE

Experience: 8 - 20 Years, We are seeking a highly skilled AI Application Engineer to design, develop, and integrate enterprise-grade agentic

AI applications. The ideal candidate will have hands-on experience working with large language models (LLMs),

multi-agent architectures, and cloud-native technologies. This role requires strong Python and AWS engineering

expertise to build scalable, secure, observable, and reliable AI agents and enterprise tool integrations., * Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

 * Proven experience developing AI applications and integrating LLMs or foundation models with enterprise

systems.

 * Strong proficiency in Python, asynchronous programming, API development, and object-oriented design.
 * Hands-on experience with LangChain, LangGraph, or similar agentic AI frameworks.
 * Strong understanding of prompt engineering, tool calling, structured outputs, context management, and LLM

response validation.

 * Experience with AWS services, relational databases, REST APIs, and cloud-native application development.
 * Familiarity with multi-agent orchestration, distributed systems, task persistence, and asynchronous workflows.
 * Strong problem-solving, debugging, communication, and collaboration skills.

AI Application Engineer

Preferred Skills

 * Experience with Amazon Bedrock AgentCore Runtime, Gateway, and Observability.
 * Knowledge of A2A protocols, MCP clients and servers, and enterprise tool integration.
 * Experience with PostgreSQL, Amazon S3, AWS Secrets Manager, OpenTelemetry, CloudWatch, Hashicorp, and

pytest.

 * Experience deploying secure, scalable, and highly available AI applications in production environments.
 * Familiarity with Docker, Kubernetes, CI/CD pipelines, retrieval-augmented generation, LLM evaluation, and AI

## Description

* Collaborate with cross-functional teams to define AI agent requirements, integration patterns, and implementation

plans.

 * Design and develop orchestration agents and domain-specific agents using Python 3.12, LangChain, LangGraph,

or Strands.

 * Integrate LLMs and foundation models for reasoning, structured responses, tool calling, and workflow automation.
 * Deploy and operate AI agents using Amazon Bedrock AgentCore Runtime and Azure OpenAI.
 * Implement agent-to-agent communication using A2A 1.0 and the official Python a2a-sdk.
 * Build secure agent-to-tool integrations using the Model Context Protocol (MCP) and Amazon Bedrock AgentCore

Gateway.

 * Use PostgreSQL for task persistence, workflow status, agent state, and idempotency management.
 * Store and manage generated documents and artifacts using Amazon S3 and secure credentials with AWS

Secrets Manager.

 * Implement logging, metrics, and distributed tracing using AgentCore Observability, OpenTelemetry, and Amazon

CloudWatch.

 * Develop unit, integration, and A2A contract tests using pytest, and troubleshoot complex AI application issues.
 * Document agent frameworks, integration patterns, development standards, and operational best practices.