Senior Ai Ml Engineer

Senior AI/ML Engineer GenAI & Cloud Solutions

Location: Woodland Hills, CA / Mason, OH (Onsite)

About Us

STAFFXPERT LLC is a leading talent solutions and workforce consulting firm connecting exceptional professionals with innovative organizations across technology, healthcare, engineering, and enterprise services. We are committed to delivering top talent that drives business transformation and innovation.

Job Summary

STAFFXPERT LLC is seeking a Senior AI/ML Engineer GenAI & Cloud Solutions on behalf of our client in Woodland Hills, CA and Mason, OH.

This role is ideal for an experienced AI/ML professional with expertise in Generative AI, Agentic AI frameworks, Large Language Models (LLMs), and Azure Cloud technologies. The selected candidate will lead the design, development, and deployment of enterprise-scale AI solutions, driving innovation through multi-agent architectures, cloud-native platforms, and advanced AI/ML engineering practices. The position requires strong technical leadership, hands-on development skills, and experience building secure, scalable, and compliant AI solutions in regulated environments.

Key Responsibilities

• Design and architect scalable, secure, and high-performing AI/ML solutions utilizing Agentic AI frameworks, Agent-to-Agent (A2A) architectures, and Model Context Protocol (MCP).

• Lead the development of enterprise Generative AI applications, including Retrieval-Augmented Generation (RAG), vector search, embeddings, prompt engineering, and context engineering.

• Architect and deploy AI/ML workloads on Microsoft Azure, ensuring scalability, security, performance, and cost optimization.

• Design and optimize data architectures leveraging Azure AI Search, Cosmos DB, Redis, Blob Storage, and Iceberg-based data platforms.

• Develop and manage cloud-native services using Azure Functions, Azure Container Apps, and distributed microservices architectures.

• Define best practices for system reliability, observability, scalability, and performance optimization.

• Ensure AI solutions comply with healthcare and data privacy regulations, including HIPAA, GDPR, and other applicable compliance standards.

• Provide technical leadership through architecture reviews, code reviews, mentoring, and engineering best practices.

• Collaborate with cross-functional teams, including product, compliance, security, and operations stakeholders.

• Research emerging AI technologies and evaluate innovative approaches to enhance enterprise AI capabilities.

Required Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.

• 8+ years of software engineering experience, including significant experience in AI/ML and Generative AI solution development.

• Strong expertise in:

  • Generative AI, LLMs, and Natural Language Models (NLMs)

  • Multi-agent systems and Agentic AI architectures

  • MCP Protocol and agent orchestration frameworks

  • Vector embeddings, prompt engineering, context engineering, and LLM fine-tuning

• Advanced proficiency in Python and experience developing production-grade AI applications.

• Hands-on experience with Microsoft Azure services for AI and cloud-native application deployment.

• Strong knowledge of Azure AI Search, Redis, Cosmos DB, and distributed data storage solutions.

• Experience designing microservices, serverless applications, and scalable cloud-native architectures.

• Knowledge of AI security, governance, and compliance requirements within regulated industries.

• Excellent problem-solving, communication, and stakeholder management skills.

• Proven experience mentoring engineers and leading technical initiatives.

Preferred Qualifications

• Master's degree in Computer Science, AI/ML, Data Science, or a related field.

• Microsoft Azure certifications such as:

  • Azure Solutions Architect Expert

  • Azure AI Engineer Associate

• Experience with frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, or similar AI orchestration platforms.

• Experience implementing MLOps, LLMOps, and CI/CD pipelines for AI workloads.

• Healthcare industry experience with regulated data environments and compliance frameworks.

• Contributions to open-source AI projects, technical publications, patents, or industry presentations.