Technical Architect

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.