Technical Architect- Remote, India

This is a remote position.

• 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.
  

• 10+ years in software engineering, data or AI roles, including at least 3 years in an architect or technical lead capacity.
  
• Demonstrated experience architecting and delivering production Generative AI systems, not only prototypes or POCs.
  
• Strong hands-on Python, with the ability to prototype designs and review production code.
  
• Deep expertise in LLM application architecture: prompt and context engineering, structured output, tool calling and orchestration.
  
• Proven experience designing RAG systems end-to-end, including chunking, embedding selection, hybrid retrieval and re-ranking.
  
• Proven ability to manage multiple enterprise AI programs simultaneously.
  
• Strong client-facing consulting experience with executive communication.
  
• Excellent presentation, whiteboarding, and workshop facilitation skills.
  
• Demonstrated experience influencing technical decisions across multiple teams.
  
• Experience managing senior engineers and mentoring future technical leaders.
  
• Strong engineering judgement balancing quality, cost, delivery timelines, and business value.
  
• Comfortable making architectural decisions with incomplete information.
  
• Experience with agent and orchestration frameworks such as LangChain, LangGraph, LlamaIndex or CrewAI.
  
• Strong knowledge of vector databases (Pinecone, Weaviate, Qdrant, FAISS, pgvector) and their operational trade-offs.
  
• Solid machine learning and deep learning fundamentals, including fine-tuning and adaptation approaches (LoRA/QLoRA, PEFT).
  
• Strong cloud architecture skills on AWS, Azure or GCP, including their AI/ML and data services.
  
• Experience with microservices, API design, event-driven patterns and enterprise system integration.
  
• Working knowledge of MLOps and LLMOps: CI/CD, containerization, model versioning, monitoring and rollback.
  
• Experience defining LLM evaluation and observability approaches (RAGAS, LangSmith, DeepEval or equivalent).
  
• Personal
  
• Strong communication and stakeholder management skills, including with non-technical and client-side audiences.
  
• Sound engineering judgement, with the confidence to defend a design and the openness to revise it.
  
• Strong ownership across the full delivery lifecycle, not only the design phase.
  
• Ability to mentor engineers and lead through influence rather than authority.
  
• Comfortable operating with ambiguity in a fast-moving technology space.
  

• 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-premise 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.
  
• Personal
  
• Proactive mindset with a genuine interest in tracking a fast-moving field.
  
• Consulting orientation, balancing technical ideals against client timelines and budgets.
  

• Proactive mindset with a genuine interest in tracking a fast-moving field.
  
• Consulting orientation, balancing technical ideals against client timelines and budgets.
  

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
  
• Relevant certifications in AI/ML, cloud architecture (AWS/Azure/GCP), or enterprise architecture are a plus.
  
• A portfolio of production Generative AI architectures, open-source contributions or published work is highly desirable.