Role Summary

We are seeking an AI &

- Machine Learning Manager with strong hands-on expertise in AI/ML engineering, Generative AI, data science and enterprise solution delivery. The role requires a techno-functional leader who can engage business stakeholders, identify high-value AI opportunities, shape solution architecture and guide engineering teams through development, deployment and adoption.

The candidate must combine sound technical judgement with business understanding. They will lead the end-to-end delivery of secure, scalable and production-ready AI solutions, while remaining close enough to the technology to review designs, challenge implementation choices and guide teams on engineering standards.

Key Responsibilities

AI Strategy &

- Business Alignment

- Work with business leaders and domain teams to identify, assess and prioritise AI/ML opportunities based on business value, feasibility, risk and expected return
- Translate business problems into clear AI use cases, functional requirements, solution options, delivery plans and measurable success criteria
- Define AI roadmaps and support business cases, proof-of-concept assessments and the transition of successful pilots into production solutions

AI/ML Solution Design &

- Delivery

- Lead the design, development, testing and deployment of machine learning, deep learning, NLP, predictive analytics and Generative AI solutions
- Guide the development of LLM-based applications, Retrieval-Augmented Generation (RAG), AI agents, conversational systems and intelligent automation solutions
- Provide hands-on technical direction across the AI lifecycle, including data preparation, feature engineering, model selection, evaluation, integration and production release
- Review solution designs, model approaches, code quality and deployment plans; resolve technical issues and guide teams on sound engineering practices

Architecture, Platforms &

- MLOps

- Define and govern scalable AI solution architectures across cloud and enterprise platforms, with attention to performance, reliability, security and cost
- Establish MLOps practices for model versioning, experiment tracking, CI/CD, deployment, monitoring, drift detection, retraining and lifecycle management
- Promote reusable components, model-serving patterns, prompt templates,



evaluation frameworks and AI accelerators to improve delivery quality and speed
- Work with data engineering, cloud, application, security and infrastructure teams to ensure reliable integration with enterprise systems and data platforms

Techno-Functional &

- Stakeholder Leadership

- Act as the bridge between business stakeholders and technical teams, ensuring that business needs are reflected in solution design and delivery
- Lead discovery sessions, requirement workshops, solution walkthroughs, demonstrations and design discussions with business and technology stakeholders
- Explain technical options, trade-offs, limitations, risks and expected outcomes in clear business terms, including to senior management
- Drive user adoption, change management and the integration of AI solutions into business processes

Team Leadership &

- Delivery Management

- Lead, mentor and develop AI engineers, ML engineers, data scientists and solution architects; set transparent technical and delivery expectations
- Own delivery planning, estimation, prioritisation, sprint execution, dependencies, risks and issue resolution across AI initiatives
- Track delivery milestones, model quality, system performance, user adoption and business impact; drive corrective action where required
- Build a culture of technical ownership, experimentation, documentation, knowledge sharing and continuous improvement

AI Governance, Risk &

- Responsible AI

- Embed responsible AI principles, privacy, security, explainability, fairness and human oversight into AI solution design and delivery
- Establish controls for model validation, data usage, access, auditability, monitoring and change management in line with enterprise policies and applicable regulations
- Work with risk, legal, compliance and information security teams to assess AI risks and support governance reviews before and after deployment

Required Qualifications &

- Experience





- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering or a related discipline
- Substantial professional experience in AI/ML engineering, data science, Generative AI or enterprise technology delivery, including experience leading technical teams or complex initiatives
- Strong hands-on knowledge of machine learning, deep learning, NLP, predictive modelling and Generative AI, with the ability to guide implementation decisions
- Strong programming proficiency in Python and practical experience with relevant AI/ML libraries and frameworks such as PyTorch, TensorFlow or scikit-learn
- Practical experience designing and delivering LLM-based applications, including prompt engineering, RAG, embeddings, vector search and AI agent patterns
- Working knowledge of model evaluation, validation, deployment, monitoring, model drift and lifecycle management
- Experience with MLOps concepts and tools for experiment tracking, model registry, CI/CD, deployment and production monitoring
- Ability to elicit and analyse business requirements, define functional and non-functional needs, and convert them into technical designs and delivery plans
- Experience working with senior stakeholders and cross-functional teams, managing expectations, explaining technical trade-offs and presenting solution recommendations
- Strong problem-solving, communication, documentation, delivery management and team leadership skills

Preferred Qualifications &

- Experience

- Experience with Microsoft Azure AI Services, Azure OpenAI, Azure Machine Learning, Azure AI Foundry, Databricks or Microsoft Fabric; equivalent AWS or Google Cloud experience is also relevant
- Hands-on exposure to LangChain, LlamaIndex, Semantic Kernel, vector databases and agentic AI frameworks
- Experience with MLflow, Azure ML, Kubeflow, Databricks Model Serving or comparable MLOps platforms
- Experience delivering enterprise solutions in BFSI, insurance, healthcare, retail, manufacturing or other regulated environments
- Knowledge of Responsible AI frameworks, model risk management, explainability, fairness testing and AI governance
- Relevant cloud, AI engineering, machine learning or Databricks certifications

📌 AI & Machine Learning Manager (Techno-Functional) (Bengaluru)
🏢 Unison Group
📍 Bengaluru