AI SDLC Architect

• Strong Python Programming experience with AI/ML frameworks such as TensorFlow, PyTorch.
• Design, build, and deploy AI agents using LangChain, CrewAI, LangGraph, AutoGen, or similar frameworks.
• Integrate large language models (LLMs) (GPT-4,o1, Claude, Llama, Mistral etc.,) into multi-agent systems.
• Develop multi-agent orchestration pipelines, enabling collaboration between AI agents for complex task execution.
• Knowledge of vector databases and embeddings for retrieval-augmented AI (RAG).
• Implement retrieval-augmented generation (RAG) with vector databases like Milvus, Pinecone, Azure AI Search (with Vector Indexing).
• Optimize AI workflows using Reinforcement Learning (RLHF) and function calling for improved reasoning and automation.
• Integrate AI agents with APIs, DevOps tools and existing workflows.
• Ensure observability, debugging, and monitoring of AI agent behavior through logging frameworks.
• Implement memory persistence for long-term agent interactions using graph-based memory or event stores.
• Collaborate with cross-functional teams to embed AI agents into DevOps, automation, and software development processes.
• Implement various prompt techniques, including zero-shot, few-shot, chain-of-thought (CoT), and fine-tuning for optimized LLM performance

Qualifications

• Strong Python Programming experience with AI/ML frameworks such as TensorFlow, PyTorch.
• Design, build, and deploy AI agents using LangChain, CrewAI, LangGraph, AutoGen, or similar frameworks.
• Integrate large language models (LLMs) (GPT-4,o1, Claude, Llama, Mistral etc.,) into multi-agent systems.
• Develop multi-agent orchestration pipelines, enabling collaboration between AI agents for complex task execution.
• Knowledge of vector databases and embeddings for retrieval-augmented AI (RAG).
• Implement retrieval-augmented generation (RAG) with vector databases like Milvus, Pinecone, Azure AI Search (with Vector Indexing).
• Optimize AI workflows using Reinforcement Learning (RLHF) and function calling for improved reasoning and automation.
• Integrate AI agents with APIs, DevOps tools and existing workflows.
• Ensure observability, debugging, and monitoring of AI agent behavior through logging frameworks.
• Implement memory persistence for long-term agent interactions using graph-based memory or event stores.
• Collaborate with cross-functional teams to embed AI agents into DevOps, automation, and software development processes.
• Implement various prompt techniques, including zero-shot, few-shot, chain-of-thought (CoT), and fine-tuning for optimized LLM performance