GenAI Architect

Roles and Responsibilities :

• Design and implement scalable AI/ML and Generative AI architectures for enterprise applications.
• Develop LLM-powered applications, autonomous AI agents, and multi-agent orchestration systems.
• Architect state management and persistent memory systems for long-running AI workflows.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases.
• Lead the evaluation, selection, and implementation of AI technologies, frameworks, and infrastructure.
• Collaborate with stakeholders to define business requirements and convert them into technical solutions.
• Design and implement AI evaluation, observability, and monitoring frameworks.
• Develop scalable APIs, microservices, and distributed AI systems.
• Deploy and manage AI solutions on cloud platforms, primarily AWS.
• Implement MLOps/LLMOps practices for model lifecycle management and deployment automation.
• Mentor engineering teams and bridge the gap between Data Science and ML Engineering teams.
• Contribute to solution proposals, RFP responses, architecture documentation, and effort estimations.
• Ensure adherence to industry best practices, security standards, and performance engineering principles.

Required Skills & Qualifications

AI & Machine Learning

• Strong expertise in Machine Learning, Generative AI, and Large Language Models (LLMs).
• Hands-on experience designing and deploying LLM-based applications and agentic AI systems.
• Experience with multi-agent orchestration frameworks such as: LangGraph, CrewAI, AutoGen, Semantic Kernel, Strong understanding of: Prompt engineering, Embeddings, Vector databases, RAG architecture, Autonomous workflow design
• Experience implementing AI evaluation and monitoring frameworks.
• Familiarity with ML pipelines and frameworks such as MLflow, Kubeflow, or similar platforms.
• Strong programming expertise in Python. Hands-on experience with: NumPy, Pandas, Scikit-learn
• Experience designing scalable microservices and distributed systems.
• Strong API development and integration experience.

Cloud & Infrastructure

• Experience deploying AI solutions on AWS.
• Familiarity with Docker and Kubernetes.
• Understanding of AI infrastructure, vector databases, and data pipelines.
• Experience with MLOps and LLMOps platforms.

Architecture & Leadership

• Expertise in distributed systems architecture.
• Strong understanding of scalability, reliability, and performance engineering.
• Ability to design enterprise-grade AI platforms and frameworks.
• Strong technical leadership and mentoring capabilities.
• Excellent analytical, communication, and stakeholder management skills.
• Ability to explain complex AI concepts to both technical and non-technical audiences.
• Strong documentation and architecture communication skills.