Technical Architect

Leewayhertz Technologies; United States; Remote; USD 150,000 - 200,000; Full time

Job summary

Leewayhertz Technologies is seeking a Senior AI Architect to own end-to-end GenAI architectures across retrieval, orchestration, model integration, and deployment layers.

You will translate business problems into concrete designs, guide solution workshops, and mentor engineers to ensure scalable, secure, and compliant implementations. The role supports pre-sales and governance, with hands-on prototyping as needed.

Qualifications

• Experience architecting multi-agent systems and autonomous workflows.
• Exposure to multimodal AI covering vision, speech, or documents.
• Familiar with inference optimization and serving at scale.
• Experience deploying open-weight models on-premises or in-VPC for data-sensitive clients.

Responsibilities

• Own the end-to-end GenAI architecture across retrieval, orchestration, model, integration, and deployment layers.
• Translate ambiguous business problems into AI solution designs with clear scope and success metrics.
• Define reference architectures, patterns and accelerators for RAG and LLM integration.
• Lead model/platform decisions with trade-offs on cost, latency, and data residency.
• Design non-functional envelope: scalability, latency budgets, availability, observability and cost control.
• Architect data and retrieval pipelines: ingestion, chunking, embeddings, vector stores, hybrid search.
• Define evaluation strategy and guardrails for accuracy, safety and groundedness.
• Embed security, privacy and compliance into the design (PII, tenancy, access control).
• Support pre-sales and discovery with solution workshops and technical proposals.
• Guide delivery teams, conduct design reviews, and mentor engineers.
• Maintain architecture docs and decision records; evaluate enterprise readiness of AI advances.
• Lead multiple client engagements, manage timelines, and ensure delivery quality.
• Drive cross-project reuse with internal frameworks and reference implementations.
• Present architecture choices and risks to executive audiences.

Skills

Multi-agent systems

Generative AI orchestration

RAG systems

Model integration

Graph retrieval

AI governance knowledge

Education

Bachelor's degree in CS/SE

Tools

Airflow

dbt

Spark

TensorRT

Job description

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.