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 strong 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.
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