AI Application Engineer / Lead

HMG America LLC is a Business Solutions focused Information Technology Company offering IT consulting and services, software and web development, staff augmentation, and other professional services. This role supports a direct client in Santa Clara, CA.

Business Objectives & Expected Outcomes

• 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.

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.
• Drive AI engineering best practices.
• Partner with R&D, product, and business stakeholders.

Technical Stack

Programming Languages

• Mandatory: Python, SQL
• 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

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, GCP

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.

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.

Requirements

• 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.

Skills

• Mandatory:
  • Python
  • LLM application development
  • RAG architecture design
  • PyTorch or TensorFlow
  • REST APIs
  • Azure cloud
  • Vector databases