AI/ML ARCHITECT

Key ResponsibilitiesHands‑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 reliabilityCloud & 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 frameworksArchitecture & 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 standardsClient & 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 strategyCOE & 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 practicesPre‑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 credibilityRequired 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 AzureExperience 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 rolesPreferred (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)