Job Description
As a GenAI Solution Architect, you will design and implement enterprise-grade Generative AI solutions that seamlessly integrate with business applications and workflows. This role spans end-to-end architecture—from building & maintaining - GenAI pipelines, prompt engineering strategies, and multi-LLM gateways to managing data lake houses, retrieval & knowledge governance frameworks. You will develop ontologies, taxonomies, and agentic workflows for autonomous reasoning while ensuring compliance, observability, and cost optimization. The ideal candidate combines deep expertise in AI/ML systems, data engineering, and enterprise integration to deliver scalable, secure, and efficient GenAI solutions that transform knowledge management and decision-making across the organization. Preference will be given to candidates with experience in both leveraging industry-leading solutions and building custom GenAI solutions from the ground up
Key Responsibilities:
1.GenAI Development & Integration
• Design and implement GenAI workflows for enterprise use cases.
• Develop prompt engineering strategies and feedback loops for LLM optimization.
• Capture and normalize LLM interactions into reusable Knowledge Artifacts.
• Integrate GenAI systems into enterprise apps (APIs, microservices, workflow engines)
• Programming languages: Python
2. Data Lakehouse & Knowledge Management
• Architect and maintain Lakehouse environments for structured and unstructured data.
• Implement pipelines for document parsing, chunking, and vectorization.
• Maintain knowledge stores, indexing, metadata governance
• Enable semantic search and retrieval using embeddings and vector databases.
3. Ontology & Taxonomy Engineering
• Build and maintain domain-specific ontologies and taxonomies.
• Establish taxonomy governance and versioning.
• Connect semantic registries with LLM learning cycles.
• Enable knowledge distillation from human/LLM feedback.
4. AI Governance & Knowledge Distillation
• Establish frameworks for semantic registry, prompt feedback, and knowledge harvesting.
• Ensure compliance, normalization, and promotion of LLM outputs as enterprise knowledge.
5. Observability & Cost Optimization
• Implement observability frameworks for GenAI systems (performance, latency, drift).
• Monitor and optimize token usage, inference cost, and model efficiency.
• Maintain dashboards for usage analytics & operational metrics.
• Make Build vs. Buy decisions based on cost-benefit analysis
6. Model Gateway & Multi-LLM Strategy
• Architect model gateways to access multiple LLMs (OpenAI, Anthropic, Cohere, etc.).
• Dynamically select models based on accuracy vs. cost trade-offs.
• Benchmark and evaluate models for enterprise-grade performance.
7. Agentic Workflows
• Design and implement agent-based orchestration for multi-step reasoning and autonomous task execution.
• Design and implement agentic workflows using industry-standard frameworks for autonomous task orchestration and multi-step reasoning.
• Ensure safe and controlled execution of agentic pipelines across enterprise systems via constraints, policies, and fallback paths.