Associate Architect - Data Science

Key Responsibilities

Define and drive enterprise-level architecture for Data Science, AI/ML, Generative AI, and Agentic AI solutions, ensuring alignment with organizational strategy and technology roadmaps

Lead the design of scalable, secure, and high-performance AI platforms, covering data, model, orchestration, and serving layers across multiple business domains

Establish architectural standards, design patterns, and reusable frameworks for Machine Learning, Deep Learning, Generative AI, and Agentic AI systems

Own and govern the end-to-end AI/ML ecosystem, including data pipelines, feature stores, model training environments, inference layers, and monitoring systems

Define and institutionalize best practices for MLOps and LLMOps, at scale, including multi-environment deployments, governance, observability, cost optimization, and lifecycle management

Architect and oversee enterprise-grade Generative AI and Agentic AI platforms, including RAG architectures, multi-agent orchestration, tool integration, memory management, and guardrails

Provide architectural oversight and technical direction across multiple teams, ensuring consistency, scalability, and reusability of AI solutions

Collaborate with senior stakeholders (Product, Engineering, Data, Security, Governance) to translate business strategy into AI driven solution blueprints

Lead technology evaluations, define platform strategies, and guide adoption of emerging tools, frameworks, and AI capabilities

Ensure compliance with AI governance frameworks, including security, privacy, ethical AI, and regulatory standards

Mentor Tech Leads and senior engineers, driving architectural maturity and capability building across the organization

Act as a key contributor in Architecture Review Boards (ARB) and strategic decision-making forums

Person Specifications

Bachelor's degree in IT, Computer Science, Software Engineering, Data Science, Engineering, Mathematics, or a related field

8–10 years of professional experience in Data Science, AI, or ML, working in production-grade environments, with significant experience in solution architecture and enterprise-scale system design

Technical Expertise

Deep expertise in Machine Learning and Deep Learning, including advanced model design, optimization, and large-scale deployment

Extensive hands-on and architectural experience in Generative AI (LLMs, RAG pipelines, embeddings, fine-tuning, evaluation frameworks)

Strong experience designing Agentic AI systems (multi-agent architectures, orchestration frameworks, tool ecosystems, autonomous decision-making)

Proven track record in implementing MLOps practices at scale (CI/CD for ML, automated pipelines, monitoring, retraining strategies)

Advanced expertise in LLMOps, including prompt lifecycle management, evaluation pipelines, guardrails, observability, latency, and cost optimization

Strong programming skills in Python and deep familiarity with AI/ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn)

Experience with cloud platforms (AWS, Azure, or GCP) and cloud?native platforms & services (e.g., Copilot Studio, Bedrock, Vertex AI, Azure OpenAI)

Strong understanding of data engineering and data platform architecture (ETL/ELT pipelines, feature stores, data lakes/warehouses)

Experience with distributed systems, microservices, APIs, and event driven architectures in AI contexts

Leadership and Architectural Skills

Strong system thinking and ability to design end-to-end enterprise AI architectures

Proven leadership in guiding multiple teams and influencing senior stakeholders

Experience defining and enforcing architecture governance, standards, and best practices

Excellent communication and stakeholder management skills, including C-level engagement

Strong focus on scalability, reliability, security, and cost-efficiency in AI systems

Ability to balance innovation with practical, production-grade delivery

Vendor submissions - 12 months

Originally posted on Himalayas