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 Vendo