Top Machine Learning Jobs in Pune

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.

The Director of Data Platform Engineering will oversee the data infrastructure, manage relational databases, lead AI integration, and ensure data governance while fostering a collaborative team environment.

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 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.

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.

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.

Support implementation of sales process and technology projects; gather requirements; maintain and improve CRM/CPQ capabilities; apply AI and automation (lead/opportunity scoring, next-best-action, conversational AI) to boost sales efficiency; partner with stakeholders and IT on integrations and AI use cases; provide global user support and issue resolution; validate changes via testing/UAT; document processes and drive adoption.

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.

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, build, and deploy ML and AI models using large structured and unstructured datasets. Develop and automate ETL/data pipelines, improve data quality, and create AI-enabled product features (NLP, deep learning, generative AI). Collaborate with product and engineering to productionize intelligent solutions that drive product usage and adoption.

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 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.

The Senior Technical Program Manager will drive execution across AI, data, and engineering initiatives, ensuring alignment, delivery rigor, and risk management.

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.

Lead design, development and maintenance of reports, dashboards and automated report generation. Own data engineering, data quality, lineage and governance; build BI platforms, cloud-native services, and drive DataOps, automation and AI-enabled solutions while engaging stakeholders and leading teams.

Provide mid-level technical support by troubleshooting and resolving customer issues, managing support tickets to meet SLAs, collaborating with cross-functional teams, improving support processes, and delivering training to junior staff. Support multiple Zebra products and use AI-driven tools to diagnose and resolve problems.

Provide intermediate-level technical support for multiple Zebra products: troubleshoot and resolve moderate technical issues, manage support tickets within SLAs, collaborate cross-functionally, contribute to process improvements, and train entry-level team members.

Perform data governance and records analysis to identify quality, lineage, and control issues; build data pipelines; apply advanced analytics and ML to large datasets; create interactive dashboards and MI reporting; conduct RCSA activities, issue management, stakeholder engagement, and advise on risk controls and remediation.

Design, develop and deliver scalable, secure software and large-scale automations (primarily UiPath). Collaborate with product, design, and engineering teams, lead or mentor mid-size technical teams, perform code reviews, enforce secure coding and unit testing, integrate REST/SOAP/web services, manage versioning and releases, and support infrastructure including VMs, credential vaults and Active Directory.

Lead design and implementation of ML tools for scenario classification, anomaly detection, sensor data quality, behavior analytics, and automated reporting. Build scalable pipelines and automated data curation for camera, radar, and LiDAR; create SIL/HIL replay interfaces, ground-truthing and annotation tools, and offline benchmarking. Integrate ML modules into simulation, HIL/SIL, and CI/CD ecosystems, implement reproducible workflows (Docker, CI/CD, artifact versioning, automated tests), monitor model/data quality and mentor junior engineers while collaborating with verification and perception teams.

Lead design, build and maintain data architectures, pipelines, warehouses and lakes; develop processing and analysis algorithms; collaborate with data scientists to deploy ML models; manage risk and lead teams or projects.

Lead design and implementation of ServiceNow ITOM Event Management and Dynatrace observability to reduce alert noise, automate incident correlation and remediation, implement observability-as-code via APIs/CI-CD/Terraform, leverage AI/ML for root-cause and predictive monitoring, and track KPIs like MTTR and alert reduction.

Lead requirements gathering, process analysis, and documentation for Legal IT systems. Support design, testing, training, stakeholder communication, and project delivery. Identify automation/AI opportunities and monitor KPIs to improve contract lifecycle efficiency and compliance.

Lead a Pune-based analytics team to deliver and govern finance BI reporting and AI/ML analytics. Manage intake, prioritize initiatives, build standardized dashboards and KPIs, ensure data governance and model lifecycle practices, champion ML/LLM automation, reduce manual work, and drive decision-ready insights for global finance stakeholders.