Senior AI/ML Engineer – GenAI & Cloud Solutions

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.

Evaluation Criteria (Critical Components)

1. Technical Depth · Ability to design and implement multi-agent AI systems.

· Experience in LLM fine-tuning, embeddings, and context engineering.

· Expertise in coding proficiency with production-grade systems in Python.

1. Architectural Vision · Ability to define enterprise-level AI/ML architecture aligned with cloud-native principles.

· Experience in scalability, resilience, and performance optimization.

1. Cloud & Data Expertise · Hands-on deployment of AI workloads on Azure Cloud.

· Strong knowledge of databases, search systems, and distributed storage.

1. Domain Knowledge · Familiarity with healthcare regulations and ability to design compliant solutions.

2. Leadership & Collaboration · Experience mentoring engineers, conducting reviews, and driving technical excellence.

· Ability to collaborate with cross-functional teams including product, compliance, and operations.

1. Innovation & Research Orientation · Evidence of staying current with GenAI advancements and applying them to real-world problems.

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.