Education: B.Tech / B.E. in Computer Science, Information Technology, Artificial Intelligence, Data Science, or related disciplines. Master's degree in AI/ML, Computer Science, or related field is preferred.

About the Role:

We are seeking a highly experienced Senior AI/ML Architect to lead the design, architecture, and implementation of next-generation AI solutions powered by Large Language Models (LLMs), Agentic AI, and Generative AI technologies. The ideal candidate will possess deep expertise in AI engineering, scalable cloud architectures, and enterprise-grade deployment practices while providing technical leadership to cross-functional teams and engaging with global stakeholders.

Key Responsibilities (KRAs):

- Design and architect enterprise-scale AI/ML and Generative AI solutions aligned with business objectives.

- Lead the development of Agentic AI applications using frameworks such as LangChain, LangGraph, or equivalent orchestration platforms.

- Define architecture for Retrieval-Augmented Generation (RAG) systems using vector databases, embeddings, and knowledge retrieval pipelines.

- Evaluate, integrate, and optimize LLMs from providers such as OpenAI, Anthropic, and other foundation model platforms.

- Design and implement Model Context Protocol (MCP), tool/function calling, and AI gateway integrations for intelligent workflows.

- Establish scalable AI architecture patterns, coding standards, and engineering best practices across projects.

- Lead cloud-native deployment of AI solutions on AWS (preferred), Azure, or GCP environments.

- Drive containerization and orchestration strategies using Docker and Kubernetes for production-grade AI systems.

- Build and oversee CI/CD pipelines, LLMOps/MLOps frameworks, and automated deployment processes.

- Implement monitoring, observability, evaluation, and performance optimization for LLM and agent-based applications.

- Ensure secure data handling, auditability, traceability, and compliance with enterprise and regulatory requirements.

- Collaborate with product owners, business stakeholders, and engineering teams to translate business requirements into scalable AI solutions.

- Provide technical leadership, architecture governance, and mentorship to AI/ML engineers and development teams.

- Conduct architecture reviews, technology assessments, and proof-of-concept initiatives for emerging AI technologies.

- Support solution estimation, technical proposals, and strategic AI roadmap planning.

- Work effectively with global and US-based stakeholders across multiple time zones.

Required Skillsets:

- Strong expertise in Python programming for AI/ML application development.

- Extensive experience with Agentic AI frameworks including LangChain, LangGraph, or equivalent platforms.

- Hands-on experience with Large Language Models (LLMs) and Generative AI technologies.

- Deep understanding of Retrieval-Augmented Generation (RAG) architectures and knowledge retrieval systems.

- Experience with vector databases and embedding models.

- Knowledge of Model Context Protocol (MCP), tool/function calling, and AI gateway implementations.

- Experience integrating APIs from OpenAI, Anthropic, and other foundation model providers.

- Strong understanding of prompt engineering, agent orchestration, and multi-agent systems.

- Experience deploying AI solutions on AWS, Azure, or GCP, with AWS preferred.

- Proficiency in Docker, Kubernetes, and cloud-native application architecture.

- Hands-on experience with CI/CD pipelines, MLOps, and LLMOps practices.

- Knowledge of AI evaluation frameworks, observability tools, and model performance monitoring.

- Experience with secure AI architecture, governance, and responsible AI practices.

- Familiarity with regulated industry standards and compliance requirements, preferably within life sciences, pharmaceuticals, or healthcare.

- Strong system design, solution architecture, and technical leadership capabilities.

- Excellent stakeholder management, communication, and presentation skills.

- Proven experience mentoring engineering teams and driving technical excellence.

Preferred Qualifications:

- Experience in clinical development, regulatory affairs, or healthcare AI solutions.

- Exposure to enterprise AI platforms and intelligent automation initiatives.

- Knowledge of modern AI governance frameworks and responsible AI implementation.

- Relevant cloud or AI certifications are an added advantage.