Top Machine Learning Jobs in Pune

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.

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

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.

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.

Lead and deliver multiple software projects, planning schedules, allocating resources, managing stakeholders, tracking risks/issues, ensuring quality/compliance, handling scope changes and escalations, and supporting proposals with estimates.

Design, develop, and maintain credit scoring and real-time decisioning models using machine learning and alternative data. Validate models via A/B and back testing, integrate into production, monitor performance, and ensure regulatory compliance while collaborating with engineering and product teams.

Lead product strategy and roadmap for AI/ML-powered BIM and AEC platforms. Define and deliver ML-driven features (generative design, predictive analytics, computer vision, intelligent document processing), build data and model pipelines, ensure governance, optimize AEC workflows, and collaborate with cross-functional teams and industry stakeholders to drive adoption and measurable outcomes.

Design and implement scalable cloud infrastructure and CI services on GitHub and Kubernetes to run millions of automated jobs. Work on job scheduling, resource management, automated recovery, metrics/alerting/storage services, and apply ML to improve performance. Collaborate across teams and time zones to deploy features across thousands of servers.

Serve as the technical bridge between product owners and engineering, translating roadmap vision into technical requirements, prioritising features, ensuring regulatory compliance, monitoring product performance, improving infrastructure and deployment pipelines, and supporting ML model deployment and data-driven decisions while collaborating with stakeholders.

The AI Data Scientist will analyze data using machine learning to provide insights for decision-making and improve operational efficiency, as well as lead and mentor teams in AI initiatives.

The AI Data Engineer will build and maintain data systems, design data architectures, collaborate with data scientists, and advise on strategies for data management and governance.

The Senior Product Manager will drive product strategy and development, collaborating with cross-functional teams to enhance customer experience, focusing on AI and SaaS solutions.

Design and build machine learning systems for Coinbase, responsibly use generative AI tools and copilots, apply human-in-the-loop practices, and deliver measurable efficiency, cost, and quality improvements while collaborating in a remote-first environment with periodic in-person surges.

The Data Modeller will analyze data issues, design data pipelines, apply advanced analytics, create visual reports, and collaborate with teams to optimize data solutions.

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 AI/ML engineering vision and roadmap for healthcare and life sciences. Oversee multiple client engagements, design and deploy scalable production-grade AI solutions (ML, NLP, deep learning, computer vision, LLMs), ensure regulatory readiness, responsible AI practices, and drive adoption and value realization while managing executive client relationships and expanding AI adoption.

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, 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.