Senior AI/ML Engineer / Architect

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 Gen

AI, 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. Gen

AI & 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. 2. Architectural Vision
• Ability to define enterprise-level AI/ML architecture aligned with cloud-native principles.
• Experience in scalability, resilience, and performance optimization. 3. Cloud & Data Expertise
• Hands-on deployment of AI workloads on Azure Cloud.
• Strong knowledge of databases, search systems, and distributed storage. 4. Domain Knowledge
• Familiarity with healthcare regulations and ability to design compliant solutions. 5. Leadership & Collaboration
• Experience mentoring engineers, conducting reviews, and driving technical excellence.
• Ability to collaborate with cross-functional teams including product, compliance, and operations. 6. 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.