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
  

• Bachelors degree in IT Computer Science Software Engineering Data Science Engineering Mathematics or a related field
• 810 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 cloudnative 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