AI Engineer

Role & responsibilities

Agentic AI & LLM Development • Build, evaluate, and optimize AI agents using frameworks such as LangChain, LangGraph, CrewAI, or AutoGen. • Implement agentic workflows, multi-agent orchestration, tool integration, state management, and multi-step reasoning patterns (e.g., ReAct). • Design robust Retrieval-Augmented Generation (RAG) pipelines, prompt tuning strategies, and context management systems. • Integrate, fine-tune, or adapt both open-source (e.g., LLaMA, Phi) and commercial foundation models (e.g., GPT-4, Claude).Deployment & MLOps • Package AI models and agentic workflows into production-ready REST APIs. • Containerize and deploy solutions using Docker and Kubernetes across cloud platforms (AWS, Azure, or GCP). • Establish MLOps pipelines covering model tracking, automated deployment (CI/CD), observability, and vector database management. • Optimize system latency, token usage, and infrastructure cost for production workloads. Cross-Functional Collaboration • Work alongside product managers, data scientists, and backend engineers to integrate AI capabilities into application ecosystems. • Conduct experiments to evaluate new AI tools, frameworks, and model architecture updates. Skills & Qualifications Required • AI & LLM Stack: Strong proficiency in Python, SQL, and GenAI frameworks (LangChain, LangGraph, CrewAI, AutoGen). • Agentic Design: Hands-on experience building agents with tool-calling capabilities, memory retention, guardrails, and structured outputs.