AI Engineer

AI Engineer

AI Engineer to build and deliver production-grade agentic AI systems for enterprise use. The engineer will develop multi-agent workflows, integrate large language models into existing enterprise systems, and support the deployment and automation needed to run them reliably and securely in production. 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 multi-agent 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 CI/CD, 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

• 3-5 years in Machine Learning, AI, or a related field, with production systems delivered.
• At least 1 year building custom Agentic AI applications
• Strong 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, WebSockets, and event-driven systems.
• Proficiency with CI/CD 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.