AI/ML Ops Engineer

Your role involves designing, developing, and optimizing autonomous AI agents to interact with systems, users, and APIs for performing complex tasks. You will be working at the intersection of LLMs, agentic frameworks, and cloud architectures to create self-improving AI workflows for automation, coding, and decision-making. Key Responsibilities: - Strong Python Programming experience with AI/ML frameworks such as TensorFlow, PyTorch. - Designing, building, and deploying AI agents using LangChain, CrewAI, LangGraph, AutoGen, or similar frameworks. - Integrating large language models (LLMs) (GPT-4, o1, Claude, Llama, Mistral, etc.) into multi-agent systems. - Developing multi-agent orchestration pipelines to enable collaboration between AI agents for complex task execution. - Implementing retrieval-augmented generation (RAG) with vector databases like Milvus, Pinecone, Azure AI Search (with Vector Indexing). - Optimizing AI workflows using Reinforcement Learning (RLHF) and function calling for improved reasoning and automation. - Integrating AI agents with APIs, DevOps tools, and existing workflows. - Ensuring observability, debugging, and monitoring of AI agent behavior through logging frameworks. - Implementing memory persistence for long-term agent interactions using graph-based memory or event stores. - Collaborating with cross-functional teams to embed AI agents into DevOps, automation, and software development processes. - Implementing various prompt techniques, including zero-shot, few-shot, chain-of-thought (CoT), and fine-tuning for optimized LLM performance. Qualifications Required: - Experience building self-improving AI agents for software development (e.g., SWE-bots, or self-improving code generation agents). - Strong algorithmic thinking, problem-solving skills, and ability to debug AI-driven workflows. - Knowledge of NLP, semantic search, embeddings, and agent memory management. - Ability to work with knowledge graphs and symbolic reasoning techniques. - Familiarity with cloud AI services (Azure AI, AWS Bedrock, Google Vertex AI) and deploying AI agents in Containerized environments like Docker, Kubernetes, AKS or Similar. - Understanding of LLMOps, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and function calling.,