About the Role :

We are looking for an experienced AI/ML Architect with 10 - 12 years of experience in designing and implementing enterprise-scale AI/ML solutions, with strong expertise in Generative AI, Large Language Models (LLMs), RAG, and Agentic AI.

The ideal candidate will be responsible for defining AI architecture, designing scalable and secure AI platforms, and translating complex business requirements into robust enterprise AI solutions.

Key Responsibilities :

- Define and architect end-to-end AI/ML and Generative AI solutions aligned with enterprise business requirements.

- Design scalable architectures leveraging LLMs, RAG, Agentic AI, AI agents, prompt engineering, and model orchestration.

- Architect enterprise-grade Retrieval-Augmented Generation (RAG) pipelines using vector databases, Elasticsearch, embeddings, document processing, and semantic search.

- Design and implement Agentic AI architectures, including multi-agent workflows, tool calling, orchestration, memory, planning, and autonomous task execution.

- Evaluate and integrate foundation models and LLM platforms such as Azure OpenAI and AWS Bedrock.

- Develop scalable AI services and APIs using Python and FastAPI.

- Design AI/ML solutions using cloud-native services across Azure and AWS.

- Architect and optimize Vector Database solutions for high-performance semantic search and retrieval workloads.

- Design solutions using Elasticsearch for enterprise search, indexing, retrieval, and AI-powered applications.

- Establish architecture standards for LLMOps/MLOps, model lifecycle management, monitoring, evaluation, observability, and deployment.

- Design secure AI platforms addressing data privacy, access control, encryption, authentication, authorization, model security, prompt injection, data leakage, and AI-specific threats.

- Drive implementation of Responsible AI principles, including explainability, transparency, fairness, governance, safety, and human oversight.

- Ensure AI solutions comply with applicable healthcare and enterprise standards, including HIPAA, FHIR, and X12/EDI where relevant.

- Design containerized AI workloads using Docker and Kubernetes.

- Architect highly available, scalable, resilient, and cost-optimized cloud infrastructure for AI/ML workloads.

- Collaborate with data scientists, ML engineers, software engineers, DevOps teams, security teams, and business stakeholders to deliver production-grade AI solutions.

- Conduct technical evaluations of emerging AI technologies, frameworks, models, and platforms and recommend appropriate solutions.

- Establish architecture documentation, technical standards, reusable components, and best practices for enterprise AI adoption.

- Provide technical leadership and mentorship to engineering and AI/ML teams.

Tech Stack :

- Generative AI, LLM, RAG, Agentic AI, Python, FastAPI, Azure OpenAI, AWS Bedrock, Vector Databases, Elasticsearch, Kubernetes, Docker, Cloud Architecture, MLOps/LLMOps.

Required Skills & Expertise :

- Strong hands-on experience with Generative AI and Large Language Models (LLMs).

- Proven experience designing enterprise-scale AI/ML architectures from concept through production.

- Strong proficiency in Python and FastAPI.

- Experience with Vector Databases, Elasticsearch, and Cloud Architecture (Azure/AWS).

- Strong understanding of Enterprise AI Security, Responsible AI, and healthcare standards (HIPAA, FHIR).

Preferred Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.

- 10 - 12 years of overall experience in software engineering, AI/ML, data engineering, or solution architecture.

- Strong communication, stakeholder management, architectural documentation, and technical leadership skills.