AI Engineer

Design and develop Agentic AI workflows using LLMs and autonomous or semi-autonomous AI agents.

Build and integrate MCP Servers and MCP Clients to connect AI agents with enterprise applications, APIs, databases, and tools.

Develop multi-agent workflows involving planning, reasoning, tool calling, memory, orchestration, and task execution.

Build production-grade LLM applications using frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, or similar technologies.

Integrate LLMs and foundation models from OpenAI, Azure OpenAI, Anthropic, Gemini, and open-source model providers.

Develop robust backend services and APIs using Python, FastAPI, Flask, or Django.

Design and implement RAG pipelines, embeddings, vector search, and enterprise knowledge retrieval systems.

Integrate AI applications with REST APIs, databases, SaaS platforms, and enterprise systems.

Implement tool and function calling with structured outputs and reliable execution workflows.

Design AI agent guardrails and validation mechanisms to ensure safe and reliable execution.

Implement authentication and authorization mechanisms for AI applications and enterprise integrations.

Build observability, tracing, evaluation, and monitoring capabilities for AI applications.

Implement human-in-the-loop mechanisms where AI decisions or actions require validation or approval.

Containerize and deploy AI applications using Docker, Kubernetes, and cloud platforms.

Work with engineering and business teams to understand enterprise use cases and translate them into working AI solutions.

Develop prototypes rapidly and subsequently harden successful solutions for production deployment.

Debug, test, optimize, and continuously improve AI applications and agent workflows.