Role & responsibilities

- Own the end-to-end architecture of GenAI solutions across the retrieval, orchestration, model, integration, and deployment layers.

- Translate ambiguous business problems into AI solution designs with clear scope, feasibility assessment, and success metrics.

- Define reference architectures, design patterns and reusable accelerators for RAG, agentic workflows and LLM integration.

- Lead model and platform selection, documenting the cost, latency, accuracy and data-residency trade-offs behind each decision.

- Design the non-functional envelope: scalability, latency budgets, availability, observability and inference cost control.

- Architect data and retrieval pipelines covering ingestion, chunking, embedding strategy, vector store selection and hybrid search.

- Define evaluation strategy and guardrails so accuracy, groundedness, safety and hallucination rates can be measured and governed.

- Embed security, privacy and compliance into the design: PII handling, tenancy isolation, access control and audit.

- Support pre-sales and discovery through solution workshops, effort estimation, technical proposals and client presentations.

- Guide delivery teams, run design reviews and mentor engineers, while staying hands-on in prototyping and unblocking hard problems.

- Maintain architecture documentation and decision records, and assess which advances in generative AI are ready for enterprise adoption.

- Own multiple client engagements simultaneously while maintaining delivery quality.

- Lead discovery workshops, challenge assumptions, and refine business requirements into technically sound solutions.

- Push back on unrealistic timelines, architectures, or requirements using engineering judgement and data.





- Build robust relationships with Team, product owners, and executive stakeholders.

- Mentor senior engineers and cultivate future architects and technical leaders.

- Lead architectural governance, design reviews, and technical decision records.

- Set engineering standards, coding guidelines, AI development best practices, and review critical code.

- Remain hands-on by building prototypes, solving difficult technical problems, and contributing production-quality code when needed.

- Drive cross-project reuse through internal frameworks, accelerators, and reference implementations.

- Present architecture, trade-offs, risks, and implementation strategy confidently to executive audiences.

Preferred candidate profile

- Experience architecting multi-agent systems and complex autonomous workflows.

- Exposure to multimodal AI covering vision, speech, or document understanding.

- Experience with inference optimization and serving at scale (vLLM, TensorRT-LLM, Triton, quantization).

- Experience deploying open-weight models on-premises or in-VPC for data-sensitive clients.

- Knowledge of graph-based retrieval (GraphRAG) and knowledge-graph modelling.

- Familiarity with AI governance and responsible AI frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).

- Experience with data platform architecture and pipelines (Airflow, dbt, Spark, lakehouse patterns).

- Pre-sales, solutioning or client-facing consulting experience in a services organisation.

- Domain depth in one or more of BFSI, healthcare, retail, supply chain or manufacturing.

- Proactive mindset with a genuine interest in tracking a fast-moving field.

- Consulting orientation, balancing technical ideals against client timelines and budgets.

📌 Technical Architect (Gurugram)
🏢 LeewayHertz Technologies
📍 Gurugram