Define the end-to-end architecture for GenAI and ML solutions, from data ingestion and feature engineering through model execution, orchestration, integration, and production deployment.
Design enterprise-grade solutions leveraging LLMs, traditional ML models, retrieval-augmented generation, agents, and AI orchestration frameworks.
Build and validate prototypes and reference implementations using Python.
Architect RAG solutions, including document ingestion, chunking, embeddings, retrieval, reranking, prompt construction, and response generation.
Design agentic AI architectures supporting tool usage, workflow orchestration, reasoning, memory, and multi-agent interactions.
Define patterns for integrating AI capabilities with enterprise applications, APIs, data platforms, event streams, and legacy systems.
Partner with Data Scientists, ML Engineers, Data Engineers, application teams, and business stakeholders to translate business use cases into scalable AI solutions.
Define reusable AI architecture patterns, frameworks, and components that can be applied across multiple enterprise use cases.
Establish patterns for prompt management, model abstraction, model routing, model versioning, and evaluation.
Design architectures for model inference, feature pipelines, model serving, and real-time or batch scoring.
Implement and guide development of Python-based AI services, APIs, pipelines, and orchestration components.
Ensure AI solutions meet requirements for security, privacy, scalability, reliability, performance, explainability, governance, and auditability.
Evaluate open-source and commercial AI frameworks and determine appropriate technologies based on enterprise requirements.
Define technical approaches for model monitoring, hallucination detection, evaluation, guardrails, observability, and human-in-the-loop controls.
Lead architecture and code reviews and provide technical guidance through implementation and production rollout.
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