Associate Lead - Data Science

About the role

Bridge Talent is looking for an Associate Lead - Data Science to lead the development and deployment of AI and machine learning solutions. The role involves hands-on work in ML, Generative AI, Agentic AI and MLOps, and collaboration with cross-functional teams to deliver scalable, secure and production-ready AI solutions. It is a hybrid position based in Colombo, Sri Lanka.

Key responsibilities

• Lead the development and deployment of AI solutions across Machine Learning, Deep Learning, Generative AI and Agentic AI use cases.
• Lead the end-to-end model lifecycle, from data ingestion and feature engineering through model development, evaluation, deployment, monitoring and retraining.
• Build and operationalize Generative AI and Agentic AI systems, including prompt engineering, orchestration frameworks, tool integration and autonomous agent workflows.
• Work with Engineering, Data Engineering, Product, QA and DevOps teams to deliver robust AI-powered solutions.
• Mentor data scientists and ML engineers and set code quality standards.
• Ensure AI solutions meet governance requirements for security, privacy, ethical AI and regulation.
• Evaluate and integrate modern AI/ML tools such as model serving frameworks, vector databases and orchestration tools.
• Turn complex business problems into scalable AI solutions and explain results to stakeholders and leadership.

Required profile

• Bachelor's degree in IT, Computer Science, Software Engineering, Data Science, Engineering, Mathematics or a related field.
• 4-5 years of professional experience in Data Science, AI or ML in production-grade environments.
• Proven ability to lead teams, mentor engineers and drive delivery in fast-paced settings.
• Strong communication skills with technical and non-technical audiences, and experience within governance frameworks.

Required skills

• Machine Learning and Deep Learning (supervised/unsupervised learning, NLP, computer vision)
• Generative AI (LLMs, prompt engineering, RAG pipelines, embeddings, fine-tuning)
• Agentic AI systems (multiagent orchestration, tool usage, autonomous workflows)
• MLOps and LLMOps (CI/CD, monitoring, retraining, guardrails, observability)
• Python and frameworks such as TensorFlow, PyTorch and Scikit-learn
• Cloud platforms (AWS, Azure or GCP) and services such as Copilot Studio, Bedrock, Vertex AI and Azure OpenAI
• Data engineering basics (ETL pipelines, feature stores, data lakes/warehouses)