Associate Architect - Data Science

About the role

Bridge Talent is hiring an Associate Architect - Data Science for a hybrid position in Colombo, Sri Lanka. The role designs and guides scalable enterprise AI and ML solutions across Machine Learning, Generative AI and Agentic AI, defines AI architecture and standards, supports MLOps/LLMOps, evaluates emerging technologies and works with engineering, data, security and business teams to deliver secure, production-ready AI solutions.

Key responsibilities

• Define and drive enterprise-level architecture for Data Science, AI/ML, Generative AI and Agentic AI solutions aligned with strategy and roadmaps
• Lead the design of scalable, secure, high-performance AI platforms covering data, model, orchestration and serving layers
• Establish architectural standards, design patterns and reusable frameworks for ML, deep learning, generative and agentic AI
• Govern the end-to-end AI/ML ecosystem: data pipelines, feature stores, model training environments, inference layers and monitoring
• Institutionalize MLOps and LLMOps best practices, including multi-environment deployments, governance, observability, cost optimization and lifecycle management
• Architect Generative AI and Agentic AI platforms, including RAG, multi-agent orchestration, tool integration, memory management and guardrails
• Provide architectural oversight across teams, collaborate with Product, Engineering, Data, Security and Governance stakeholders, and lead technology evaluations
• Ensure compliance with AI governance, security, privacy, ethical AI and regulatory standards
• Mentor Tech Leads and senior engineers, contribute to Architecture Review Boards and strategic forums

Required profile

• 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 in production environments, with significant experience in solution architecture and enterprise-scale system design
• Proven leadership in guiding multiple teams and influencing senior stakeholders
• Experience defining and enforcing architecture governance, standards and best practices

Required skills

• Machine Learning and Deep Learning, including large-scale deployment
• Generative AI (LLMs, RAG pipelines, embeddings, fine-tuning, evaluation)
• Agentic AI systems (multi-agent architectures, orchestration, tool ecosystems)
• MLOps and LLMOps at scale (CI/CD for ML, automated pipelines, monitoring, retraining)
• Python and AI/ML frameworks such as PyTorch, TensorFlow and Scikit-learn
• Cloud platforms (AWS, Azure or GCP) and services such as Copilot Studio, Bedrock, Vertex AI, Azure OpenAI
• Data engineering: ETL/ELT pipelines, feature stores, data lakes/warehouses
• Distributed systems, microservices, APIs and event-driven architectures