Top Machine Learning Jobs in Pune

Lead technical design and build scalable data pipelines and platforms to support AI Ops, SRE, and analytics. Set standards for quality, security, and observability, mentor engineers, operationalize models, and contribute to roadmap and documentation for the ONE Data Lake.

Lead and deliver end-to-end, high-performance, secure software systems. Define architecture, drive technical decisions, mentor engineers, manage SDLC and SLAs, handle incidents, evaluate emerging technologies, and ensure compliance and quality across projects.

Design and implement technical features, collaborate with clients, write production-ready code, ensure quality deliverables, mentor team members, and implement ML models on large scale.

Lead design and implementation of an Analytic Foundation and scalable big-data services delivered via APIs. Build data pipelines, analytics and data models, responsive UIs, and production ML/data-driven applications. Ensure high-quality, well-tested code, mentor junior engineers, drive process improvements, and collaborate with product and design teams to deliver customer-focused data solutions.

Lead large-scale digital transformation programs for supply chain and manufacturing clients using AI/ML, Industry 4.0/5.0, IoT/IIoT, cloud, and automation. Advise C-level stakeholders, architect digital solutions (digital twins, predictive analytics, MES/SCADA integrations), manage multi-geography teams, drive change management, and support pre-sales and practice growth.

Lead the development of generative AI systems, overseeing multi-agent AI architectures, machine learning pipelines, and implementing responsible AI practices. Drive innovation in AI applications using various frameworks and databases while managing cross-functional teams.

Develop statistically effective algorithms and apply machine learning, deep learning, and generative AI techniques to high-dimensional problems. Use hypothesis testing, data mining, and big-data tools to identify trends, build predictive models and recommender systems, collaborate with clients and stakeholders, and evaluate emerging datasets and technologies for scalable analytical solutions.

The Engineering Lead will design and implement technical features, work on ML pipelines, and mentor engineers while ensuring high-quality deliverables. This role requires collaborating with teams and managing the ML lifecycle for business value.

Design and build production-grade AI applications embedding ML/LLM capabilities. Integrate trained models into APIs, services, UIs, and workflows. Operationalize models (versioning, deployment, monitoring), implement guardrails, ensure security/privacy/compliance, apply bias mitigation and explainability, and mentor team members on AI engineering best practices.

Lead GBSC data engineering and analytics initiatives: design data/ETL infrastructure, implement automation, enable large-scale structured and unstructured data analysis, manage team delivery, and communicate insights to influence business decisions.

Lead and grow a data science team to deliver predictive and ML-driven insights. Partner with business and engineering to build production-ready models, enforce model governance, ensure explainability, and measure business impact while driving continuous improvement.

Build data-driven solutions and BI using Python, Power BI, and SQL. Implement DevOps pipelines with GitHub Actions/Azure DevOps, deploy on Azure/AWS, integrate APIs, apply unit testing and automation, and collaborate in Agile/Scrum with global teams. Support solution design, effort estimates, and use ML/DL/LLMs for intelligent features.

Define and own end-to-end product architecture for Mastercard's switching and network services. Collaborate with product and engineering teams to design scalable, resilient, secure systems, apply AI/LLM capabilities, produce architecture artifacts, validate technical feasibility, and guide implementation while engaging stakeholders across regions and customers.

Lead automation and analytics for internal audit: design Power BI dashboards, data analytics, and AI/RPA pilots to enhance SOX testing, control validation, anomaly detection, and continuous monitoring. Liaise with IT, business, and external auditors; coach team members; support remediation and audit transformation.

Lead multi-stream data and AI projects, own project plans, budgets and risks, run governance meetings, coordinate Architects/Data Engineers/AI teams, apply Snap's delivery methodology, manage senior stakeholders, and oversee multiple concurrent client engagements to ensure timely delivery and client satisfaction.

Design, build, and maintain scalable data pipelines and cloud data platforms to support AI/ML workloads. Deploy, monitor, and operationalize machine learning models, implement CI/CD and MLOps, ensure data quality, scalability, and governance while collaborating with data scientists, engineers, and stakeholders.

The role involves conducting AI research, teaching AI-related courses, collaborating on healthcare data modeling, and securing external funding, while mentoring students.

The Postdoctoral Research Associate will lead research in structural health monitoring of transportation infrastructure, manage research activities, analyze data, and support proposal writing and public engagement events.

Design, build, and deploy AI/GenAI applications, intelligent automation, AI agents, and process intelligence solutions; analyze processes using process mining; integrate AI with enterprise systems; drive AI adoption, mentor colleagues, and influence automation strategy and architecture to improve operational efficiency.

Perform CFD, FEM and multiphysics simulations, analyze and validate results against experiments, collaborate with product teams to inform designs, and produce technical reports and presentations to communicate findings.

Design, develop and deliver enterprise-grade Pega solutions. Collaborate across teams, ensure code quality, integrations (REST/SOAP/Kafka/MQ), performance tuning, security, DevOps pipeline ownership, production stability and lead distributed Agile teams.

Lead data science initiatives using advanced statistical techniques and machine learning to produce actionable insights. Partner with cross-functional teams to design data-driven solutions, manage and ensure integrity of large datasets, develop predictive models to forecast trends, and communicate results to stakeholders while staying current on emerging technologies.

Design and implement scalable backend services, lead full software lifecycle, write design docs, mentor engineers, collaborate cross-functionally, improve performance and quality, and integrate AI tools to boost development productivity.

Lead development and maintenance of data and records governance frameworks, monitor data quality and compliance, run analytics and reporting for the Product Office, apply advanced analytics/ML to governance and incidents, develop records management program, advise stakeholders, lead or coach teams, and deliver training and governance guidance across the bank.

Lead a team of data scientists in developing and deploying machine learning models, ensuring code quality, documentation, and smooth industrialization of projects.