MLOps Engineer
Openkyber
Role: AI Application Engineer / Lead Location: Santa Clara, CA (Onsite) Job description: AI Application Engineer / Lead Role Requirements & Hiring Criteria Location Experience Priority Santa Clara, CA (Onsite) 5 8 yrs SWE; 3+ yrs AI/ML; 1 2 yrs GenAI Production AI / Agentic AI 1. Business Objectives & Expected Outcomes Business Objectives Build enterprise-grade AI applications that improve engineering, R&D, manufacturing, and knowledge management workflows. Accelerate adoption of Agentic AI across Applied Materials. Establish reusable AI platform components and frameworks. Reduce development effort through AI-assisted workflows and reusable services. Expected Outcomes Deploy production AI applications used by multiple business units. Deliver measurable productivity improvements. Create reusable RAG, agent, and orchestration frameworks. Improve knowledge discovery and decision support across engineering teams. 2. Detailed Job Description & Key Responsibilities AI Application Development Design and build AI-powered applications using LLMs and foundation models. Develop RAG solutions leveraging enterprise knowledge sources. Build multi-agent systems for complex workflows. Agentic AI Design planning, reasoning, tool-calling, and workflow orchestration systems. Build autonomous and human-in-the-loop agent architectures. Develop domain-specific AI copilots. AI Engineering Fine-tune, evaluate, and optimize models. Implement prompt engineering and evaluation frameworks. Build API services for AI model consumption. Leadership Lead technical solution design. Mentor junior engineers. Driving AI engineering best practices. Partner with R&D, product, and business stakeholders. 4. Technical Stack, Frameworks & Programming Languages Category Required / Preferred Stack Programming Languages - Mandatory Python; SQL Programming Languages - Preferred TypeScript; JavaScript; C++ AI Frameworks PyTorch; Hugging Face Transformers; TensorFlow; MLflow Agent Frameworks LangGraph; LangChain; Semantic Kernel; AutoGen Vector Databases Azure AI Search; Elasticsearch/OpenSearch; Chroma; PGVector Backend FastAPI; REST APIs; gRPC (preferred) Data Platforms Databricks; Fabric; PostgreSQL 5. Cloud Environment Primary Microsoft Azure / AWS Services Azure AI Foundry Azure OpenAI Azure AI Search Azure Functions Azure Kubernetes Service (AKS) ADLS Gen2 Preferred Additional Experience AWS Google Cloud Platform 6. Security, Compliance & Data Classification Mandatory Understanding of enterprise security controls. Experience handling Internal and Confidential data. Secure API design. RBAC and identity management. Preferred Responsible AI implementation. Data governance frameworks. Model monitoring and auditability. PII protection and redaction. AI risk assessment and guardrails. 7. Expected Deliverables & Success Criteria First 6 Months 1 2 production AI applications. Enterprise RAG framework. Agent orchestration framework. Evaluation and observability dashboards. First 12 Months Multiple production deployments. Reusable AI platform components. Reduced deployment time and development effort. Adoption across multiple teams. Success Metrics User adoption. Productivity impact. Response quality. Hallucination reduction. Platform reusability. Deployment velocity. 10. Required Years of Experience Mandatory 5 8 years Software Engineering 3+ years AI/ML Engineering 1 2 years Generative AI Preferred 2+ years building production GenAI systems. Experience leading technical workstreams. 11. Mandatory vs Preferred Skills Mandatory Python LLM application development RAG architecture design PyTorch or TensorFlow REST APIs Azure cloud Vector databases AI evaluation techniques Preferred Multi-agent systems Scientific AI Model fine-tuning Multimodal AI MLOps Databricks Kubernetes MCP ecosystem Certifications (Preferred) Azure AI Engineer Associate Azure Solutions Architect Databricks ML Professional AWS ML Specialty 12. Prior Experience with Agentic AI, LLMs & Production Deployments Mandatory Experience - LLMs GPT-family models Claude Llama Mistral Gemini RAG Chunking strategies Embedding generation Hybrid retrieval Reranking Evaluation methodologies Agentic AI Tool calling Function calling Workflow automation Memory management Planning and execution frameworks AI Orchestration Frameworks - Experience with at least one LangGraph Semantic Kernel AutoGen CrewAI Production Deployment CI/CD for AI applications Monitoring and observability Prompt versioning Model lifecycle management Cost optimization
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Vacancy posted 2 days ago
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