Job Summary

We are seeking a AI Engineer to build and deliver production grade agentic AI systems for enterprise use The engineer will develop multiagent workflows integrate large language models into existing enterprise systems and support the deployment and automation needed to run them reliably and securely in production

Responsibilities

This is a hands on engineering engagement The work centers on building agents orchestration logic and supporting infrastructure that performs under real production workloads not on proof of concept or advisory work

Scope of Work

Build AI agents and multiagent systems using frameworks with LangGraph and LangChain tools

Develop and tune prompt engineering workflows across multiple LLMs GPT Claude LLaMA balancing reliability cost and latency

Develop REST APIs WebSocket services and event driven pipelines for real time AI services that remain stable under load

Automate testing and releases through Jenkins CICD and maintain code and documentation standards using Git Jira and Confluence

Deployment of AI Application in enterprise adhering to best practices

Use AI augmented development tools such as Claude Code and Codex to accelerate delivery

Coordinate with platform security and product teams to deliver scalable secure deployments

Must Have Skills





3 to 5 years in Machine Learning AI or a related field with production systems delivered

At least 1 year building custom Agentic AI applications

Robust Python skills and sound modern development practices

Hands on experience with LLMs and prompt engineering across the full application lifecycle

Demonstrated experience building AI agents with LangGraph

Familiarity with at least one enterprise cloud AI platform for building and deploying agentic applications such as Azure AI Foundry AWS Bedrock or Google Gemini Enterprise including cloud native deployment practices

Working knowledge of REST APIs Web Sockets and event driven systems

Proficiency with CICD tooling Jenkins and version control Git

Fluency with AI augmented development tools for rapid prototyping

Strong written and verbal communication an analytical approach to problem solving and the ability to work independently within a cross functional team

Data layer curations and integration with source system for agentic application

Good to Have Skills

Familiarity with Databricks

Exposure to MLOps LLMOps workflows and application monitoring

Knowledge of enterprise security compliance and governance for AI systems

Familiarity with code and model lifecycle management practices

📌 Data Scientist (Bengaluru)
🏢 Cognizant
📍 Bengaluru