AI Application Engineer / Lead

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.
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• 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.
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• 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
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• 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