AI/ML Engineer Agentic AI

Overview

Key Responsibilities

• Design, develop, and deploy AI/ML solutions for real-world business use cases.
• Build and optimize Agentic AI systems using LLMs, autonomous workflows, and multi-agent architectures.
• Develop machine learning models for prediction, classification, recommendation, NLP, and automation tasks.
• Work with orchestration frameworks such as LangChain, CrewAI, AutoGen, LangGraph, or similar technologies.
• Integrate AI agents with APIs, databases, enterprise systems, and third-party tools.
• Fine-tune, evaluate, and optimize Large Language Models (LLMs).
• Develop Retrieval-Augmented Generation (RAG) pipelines and vector search implementations.
• Collaborate with data engineers, software developers, and product teams to deliver scalable AI solutions.
• Perform prompt engineering, model experimentation, and performance optimization.
• Ensure AI solutions are production-ready, scalable, secure, and cost-efficient.
• Monitor model performance, drift, and continuous improvement processes.
• Stay updated with emerging AI, Generative AI, and Agentic AI trends and technologies.

Required Skills & Expertise

• Strong hands-on experience in Machine Learning, Generative AI, and Agentic AI development .
• Proficiency in Python and AI/ML development frameworks.
• Experience with:
• LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks
• OpenAI, Anthropic, Gemini, or open-source LLMs
• RAG architectures and Vector Databases
• Prompt Engineering & AI workflow automation
• Solid understanding of:
• NLP concepts
• ML algorithms and model evaluation
• Deep Learning fundamentals
• AI model deployment and inference optimization
• Experience with ML libraries/frameworks such as:
• PyTorch
• TensorFlow
• Scikit-learn

• Hugging Face Transformers

• Experience with cloud platforms such as Azure, AWS, or GCP is preferred.
• Familiarity with Docker, APIs, CI/CD, and MLOps practices.
• Strong problem-solving and analytical skills.