Ai Ml Engineer

Skills and attributes for success

• Hands-on experience with Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and agent orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, or similar technologies.
• Strong understanding of Generative AI, NLP, machine learning concepts, and experience integrating AI services into enterprise applications and business workflows.
• Proficiency in Python and experience integrating enterprise systems through APIs, SDKs, and cloud-native services to develop AI-powered automation solutions.
• Familiarity with cloud platforms such as Microsoft Azure, AWS, or Google Cloud, including AI/ML services, vector databases, embeddings, and semantic search technologies.
• Understanding of AI governance, Responsible AI principles, security, privacy, and operational practices such as DevOps, MLOps, and LLMOps is an added advantage.
• Strong analytical, problem-solving, and communication skills, with the ability to collaborate effectively across AI, engineering, operations, platform, and business teams to deliver impactful AI solutions.

Your Key Responsibilities

• Design, develop, test, and deploy Agentic AI solutions leveraging LLMs, retrieval systems, AI agents, and orchestration frameworks to automate business and operational processes.
• Integrate LLMs, NLP, Computer Vision, and other AI/ML services with enterprise applications and platforms through APIs, SDKs, and cloud-native technologies.
• Develop custom applications, services, and automation workflows using Python and modern AI frameworks to enable intelligent process automation and system integration.
• Monitor, troubleshoot, maintain, and optimize AI agents and automated workflows to ensure reliability, scalability, performance, and security.
• Collaborate with AI Platform, Engineering, Operations, and Business teams to identify use cases, implement AI solutions, and operationalize enterprise AI capabilities.
• Create technical documentation, contribute to reusable AI accelerators and best practices, and ensure solutions comply with security, governance, risk management, and Responsible AI standards.