AI/ML Engineer

Programmers.io; Los Angeles (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Programmers.io is seeking an experienced AI/ML Architect to lead the design of scalable, secure GenAI systems. You will drive end-to-end solution development including embeddings, prompts, and context engineering, while overseeing cloud deployment on Azure and data pipelines with Cosmos DB, Redis, and Iceberg.

You will mentor teams, review designs, and stay ahead of GenAI trends to deliver enterprise-grade solutions with HIPAA/GDPR compliance in mind.

Qualifications

• Experience designing scalable AI/ML architectures.
• Expertise in vector embeddings, prompt engineering, and context engineering.
• Strong knowledge of GenAI, LLMs, and NLP concepts.
• Proficiency in Python; exposure to Java/Go is a plus.
• Hands-on Azure cloud deployment, monitoring, and scaling.

Responsibilities

• Architect and design scalable AI/ML systems using Agentic Layer A2A frameworks and MCP Protocol.
• Drive end-to-end solution development including vector embeddings, prompt engineering, and context engineering.
• Deploy AI/ML workloads on Azure Cloud with security, scalability, and cost optimization.
• Design data pipelines and storage using Azure AI Search, Redis, Cosmos DB, Blob Storage, and Iceberg.
• Build and manage Azure Functions and Azure Container Apps for microservices-based AI solutions.
• Define cloud-native architecture patterns and ensure performance and resilience.
• Apply healthcare-domain knowledge to meet regulatory standards (HIPAA, GDPR).
• Mentor engineering teams, conduct design/code reviews, and promote best practices.
• Stay ahead of GenAI/LLM trends and integrate cutting-edge approaches.

Skills

Agentic Layer

AI/ML Engineering

GenAI & LLM Concepts

Python Programming

Azure Cloud

Azure AI Search

Redis

Cosmos DB

Cloud-Native Architecture

Healthcare Domain

Education

Bachelor’s or Master’s in CS/AI/ML

Tools

Azure Functions

Azure Container Apps

Job description

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

Key Responsibilities:

• Architect and Design: Lead the design of scalable, secure, and high-performance AI/ML systems leveraging Agentic Layer A2A frameworks and MCP Protocols.
• Solution Engineering: Drive end-to-end solution development including vector embeddings, prompt engineering, and context engineering for enterprise-grade GenAI applications.
• Cloud Deployment: Architect and oversee deployment of AI/ML workloads on Azure Cloud, ensuring compliance, scalability, and cost optimization.
• Data Architecture: Design and optimize data pipelines and storage solutions using Azure AI Search, Redis, Cosmos DB, Blob Storage, and Iceberg.
• Application Development: Build and manage Azure Functions and Azure Container Apps for microservices-based AI solutions.
• Performance & Scalability: Define cloud-native architecture patterns, implement performance tuning, and ensure resilience across distributed systems.
• Domain Expertise: Apply deep knowledge of healthcare domain requirements, ensuring solutions meet regulatory standards (HIPAA, GDPR, etc.) and handle sensitive data securely.
• Technical Leadership: Mentor engineering teams, establish best practices, and conduct design/code reviews.
• Innovation & Research: Stay ahead of emerging GenAI, LLM/NLM trends, and integrate cutting-edge approaches into enterprise solutions.

Required Skills & Expertise:

• Agentic Layer & Protocols: Hands-on expertise with Agentic Layer A2A frameworks and MCP Protocol for multi-agent orchestration.
• AI/ML Engineering: Strong background in vector embeddings, prompt engineering, context engineering, and fine-tuning LLMs.
• GenAI & LLM Concepts: Deep understanding of Generative AI, Natural Language Models (NLM), and Large Language Models (LLM).
• Programming: Advanced proficiency in Python; exposure to Java/Go is a plus.
• Cloud Proficiency: Strong experience with Azure Cloud services, including deployment, monitoring, and scaling.
• Databases: Expertise in Azure AI Search, Redis, Cosmos DB; familiarity with Blob Storage and Iceberg is advantageous.
• Cloud-Native Architecture: Solid grasp of microservices, containerization, serverless computing, scalability, and performance optimization.
• Healthcare Domain: Experience working with regulated data environments and compliance frameworks.

Preferred Qualifications:

• Bachelors or master’s in computer science, AI/ML, or related field.
• Certifications in Azure Solutions Architect or AI Engineering.
• Publications, patents, or contributions to open-source AI/ML projects.