Machine Learning Manager (AI)

Unison Group; Bengaluru; Presencial; INR 2.000.000 - 4.000.000; Jornada completa

Descripción de la vacante

Unison Group in Bengaluru seeks an AI & Machine Learning Manager with hands‑on expertise in AI/ML engineering, Generative AI and enterprise solution delivery. You will lead end‑to‑end AI initiatives, shape architecture and guide teams from development to production across secure, scalable systems.

You will engage stakeholders, translate business problems into AI use cases, and drive adoption while upholding responsible AI practices and strong engineering standards.

Formación

• Bachelor's or Master's degree in CS, AI, DS, or engineering.
• Substantial professional experience in AI/ML engineering and data science.
• Strong hands-on knowledge of ML/DL, NLP, predictive modelling and Generative AI.
• Strong Python programming with PyTorch, TensorFlow, scikit-learn.
• Experience delivering enterprise AI solutions and leading technical teams.
• Experience with LLM-based applications, RAG, embeddings, vector search and AI agents.
• Knowledge of MLOps, model versioning, CI/CD, monitoring and lifecycle management.

Responsabilidades

• Lead end-to-end delivery of secure, scalable AI solutions.
• Shape solution architecture and guide engineering teams through development and deployment.
• Engage business stakeholders and translate problems into AI use cases and measurable success criteria.
• Define AI roadmaps and support pilots to production solutions.
• Embed responsible AI principles including privacy, security and explainability.

Conocimientos

AI strategy

Leadership

Stakeholder management

Hands-on ML engineering

Python programming

MLOps

Cloud platforms

Communication

Model evaluation & deployment

Educación

Bachelor's or Master's in CS/AI/DS/Engineering

Herramientas

PyTorch

TensorFlow

scikit-learn

LangChain

LlamaIndex

Semantic Kernel

MLflow

Azure ML

Databricks

Descripción del empleo

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 clear 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.

• 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.

Core Technology Landscape

Python | PyTorch | TensorFlow | scikit-learn | NLP | Deep Learning | Predictive Analytics | Generative AI | LLMs | Azure OpenAI | Azure AI Foundry | Azure Machine Learning | Databricks | MLflow | RAG | Embeddings | Vector Databases | LangChain | LlamaIndex | Semantic Kernel | AI Agents | Prompt Engineering | MLOps | Model Serving | Git | CI/CD | Azure DevOps | Cloud Platforms | Power BI | Microsoft Fabric