AI/ML Architect

AI/ML Architect

Hands‐On AI Solution Development (Core Requirement)

• Design, develop, and implement AI / GenAI solutions using Python as the primary programming language
• Build production‐ready AI components, including:
• Data ingestion and preprocessing pipelines
• Prompt engineering and prompt orchestration layers
• Retrieval‐Augmented Generation (RAG) pipelines
• API‐based AI services and microservices
• Write and review Python code for:
• Model integration and inference
• LLM orchestration (agents, tools, workflows)
• Data transformations and feature engineering
• Debug, optimize, and harden AI solutions for performance, scalability, and reliability

Cloud & Hyperscaler AI Implementation

• Implement AI solutions using:
• AWS (Bedrock, SageMaker, Lambda, API Gateway, Amazon Q, OpenSearch, S3, etc.)
• Azure (Azure OpenAI, Azure AI Studio, Cognitive Services, Azure ML, Functions, etc.)
• Develop cloud‐native AI architectures leveraging Python‐based SDKs and APIs
• Integrate AI services with enterprise data platforms, applications, and security frameworks

Architecture & Delivery Leadership (Hands‐On)

• Own end‐to‐end AI solution architecture, from design through deployment
• Actively participate in:
• POCs and pilots (hands‐on build)
• Production deployments (implementation support)
• Code reviews and design reviews
• Define and implement MLOps / LLMOps patterns, including CI/CD, monitoring, and observability
• Ensure solutions meet Responsible AI, security, and compliance standards

Client & Stakeholder Engagement

• Work directly with client technical and business stakeholders to:
• Translate business requirements into working AI solutions
• Demonstrate prototypes and explain implementation decisions
• Serve as the technical AI face for clients—credible because of hands‐on depth, not just strategy

COE & Team Enablement

• Mentor onshore and offshore engineers through pair programming, code reviews, and design sessions
• Define reusable Python frameworks, accelerators, and templates for AI delivery
• Contribute to AI COE standards, reference architectures, and best practices

Pre‐Sales & Growth Support

• Support pre‐sales by:
• Building working demos and POCs (not just slides)
• Reviewing technical feasibility and effort estimates
• Contribute to proposals with implementation‐level credibility

Required Qualifications

• Strong hands‐on Python development experience (non‐negotiable)
• Proven experience building and deploying AI / ML / GenAI solutions, not just designing them
• Experience with:
• LLMs, prompt engineering, RAG, agents, embeddings
• REST APIs, SDK‐based integrations, microservices
• Cloud‐native development on AWS and/or Azure

Experience Profile

• 10–15+ years overall experience with significant recent hands‐on AI work
• 5+ years delivering AI / ML / Advanced Analytics solutions in production
• Experience working in client‐facing, onshore roles

Preferred (but not mandatory)

• Experience with FastAPI / Flask for Python‐based AI services
• Familiarity with vector databases, search, or document intelligence
• Hyperscaler AI certifications (AWS / Azure)