HMG America LLC is the best Business Solutions focused Information Technology Company with IT consulting and services, software and web development, staff augmentation and other professional services. One of our direct clients is looking for AI Application Engineer / Lead in Santa Clara, CA. Below is the detailed job description.
Role: AI Application Engineer / Lead
Location: Santa Clara, CA (Onsite)
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
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