MLOps Technical Architect

We are seeking an experienced MLOps Technical Architect to lead the architecture, design, and deployment of enterprise AI/ML, Generative AI, and Agentic AI solutions. The ideal candidate will have strong expertise in MLOps, cloud-native AI platforms, LLMs, RAG architectures, and AI agent frameworks, with the ability to deliver scalable, production-ready AI solutions. Technical Skills Strong programming experience in Python and Java. Hands-on experience with Agentic AI frameworks, including Google ADK, A2A, LangChain/LangGraph, CrewAI, Semantic Kernel/AutoGen, and OpenAI Agent SDK. Experience integrating Gemini Tools and Custom MCP (Model Context Protocol) Tools. Strong knowledge of TensorFlow, PyTorch, and AutoML for machine learning model development. Hands-on experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Natural Language Processing (NLP). Experience designing and implementing RAG architectures, including data ingestion, retrieval, hybrid search, and response generation. Strong experience with Google Cloud Platform (GCP), Vertex AI, and Kubeflow. Experience in data preprocessing, feature engineering, and ML pipeline development. Proficiency with GitHub for source control and version management. Experience in model development, testing, validation, deployment, and monitoring. Strong knowledge of databases including Oracle, DB2, PostgreSQL, BigQuery, Cassandra, and Big Data platforms. Experience working in Agile/Scrum environments. Good to Have GPU programming and performance optimization. GPU profiling and TensorRT optimization. Experience with vector databases and AI model optimization techniques. Roles and Responsibilities Collaborate with business and IT stakeholders to understand business requirements and identify AI/ML opportunities. Architect scalable AI/ML, MLOps, Generative AI, and Agentic AI solutions. Lead the design and development of machine learning models, AI pipelines, and intelligent agent workflows. Develop, optimize, and automate ML models, pipelines, and orchestration logic. Design and deploy LLM-powered applications, RAG pipelines, AI agents, and vector-based memory systems. Build integrations with enterprise systems using APIs, Gemini tools, and MCP-based integrations. Work closely with Data Scientists, ML Engineers, DevOps, and Software Engineering teams to ensure successful deployment and operational excellence. Drive technical architecture, infrastructure, tooling, and cloud strategy for AI platforms. Monitor solution performance, troubleshoot production issues, and implement continuous improvements. Provide timely project updates, technical guidance, and documentation to stakeholders and leadership. Identify opportunities for process automation and operational efficiency. Foster collaboration across cross-functional teams to ensure successful project delivery. Preferred Qualifications Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Experience designing enterprise-scale AI/ML and MLOps platforms. Familiarity with cloud-native AI deployment and CI/CD practices for machine learning. #J-18808-Ljbffr