Top Machine Learning Jobs in Pune

Leads strategy and execution for Mastercard’s next-generation disputes platform. Owns strategic platform domains, roadmaps, backlogs, product requirements, investment priorities, and outcomes across multiple agile teams. Partners with executives, customers, engineering, architecture, data science, design, and regional stakeholders. Drives AI, automation, predictive decisioning, and generative AI adoption across dispute resolution experiences and operations while coaching product teams and influencing enterprise modernization and portfolio investment decisions.

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.

Designs and develops AI and machine learning models, including NLP and computer vision systems, and deploys them into scalable production applications through APIs, microservices, and cloud platforms. Responsibilities include data preparation, model training, testing, debugging, performance optimization, and collaboration with data scientists and software engineers. The role requires strong programming, ML framework, cloud, SQL, and algorithm development skills.

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.

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 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 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.

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.

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.

Sell SailPoint's IGA identity security SaaS to $5B+ enterprises by engaging C-level stakeholders, managing long sales cycles with POCs/RFPs, collaborating with GSIs and channel partners, developing account and opportunity plans, and meeting quota using Challenger methodology.

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.

Implements data and records governance across Barclays’ UK business, partnering with business units, operations, architecture, and technology teams. Responsibilities include data classification, retention, lineage, quality monitoring, risk and control management, regulatory reporting support, root-cause analysis, remediation planning, and governance policy implementation. The role develops dashboards and strategies to improve data reliability, prioritizes data risks, supports regulatory commitments, and drives stakeholder-led change in a regulated banking environment.

Lead the technical vision, architecture, development, deployment, and maintenance of enterprise applications across Clinica and Transparency platforms. Manage and mentor engineering teams, oversee delivery planning and quality assurance, collaborate with stakeholders, and advance AI, machine learning, cloud modernization, DevOps, CI/CD, monitoring, and operational excellence initiatives.

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 simulation-driven product designs using DfAM, topology optimization, CFD, heat-transfer analysis, and finite element methods. Optimize designs for additive manufacturing printability, investigate failure modes, validate models against experimental data, and refine simulation methods. Collaborate with product development and business teams on requirements and specifications, then communicate findings through technical reports and presentations.

Analyze complex financial, project, and portfolio datasets; investigate data quality, lineage, and control issues; build data pipelines; apply advanced analytics and AI; create dashboards and automate reporting. Translate findings into actionable recommendations, support forecasts and demand tracking, maintain governance artifacts, resolve queries, and partner with stakeholders across the organization. The role also involves risk management, control strengthening, and influencing operational improvements.

Leads the design, development, and delivery of scalable enterprise software, integrations, APIs, microservices, and cloud-native platforms. Builds automation and intelligent workflow solutions using Python, AI, and machine learning. Oversees infrastructure, deployment pipelines, monitoring, security, testing, operational support, and third-party connectivity. Collaborates with product, engineering, and business stakeholders, mentors teams, manages risk and controls, and drives engineering excellence across the organization.

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

Designs and supports data platform blueprints, semantic models, ontologies, taxonomies, metadata schemas, and knowledge graphs. Collaborates with integration and data architects to integrate systems and data models. Develops scalable linked-data architectures using RDF, OWL, SKOS, and SHACL, and works with graph databases and query languages. Supports semantic search, data extraction, AI-driven recommendations, and data discovery while communicating complex concepts to technical and non-technical stakeholders.

Design, develop, and improve scalable software solutions using GenAI and LLM technologies. Collaborate with product managers, designers, and engineers; participate in code reviews; apply secure coding and unit testing practices; and contribute to technical excellence. The role requires AWS, Python, Agentic AI, LLM, and GenAI expertise, with valued experience in DevOps, Go, RAG, LangGraph, watsonx, CrewAI, and machine learning. Based in Pune.

Support, enhance, troubleshoot, and maintain production AI and Generative AI applications. Responsibilities include customer onboarding, defect resolution, machine learning and GenAI validation, release management, monitoring, automation, AI governance, compliance documentation, reporting, and stakeholder communication. The role requires hands-on experience with Python, LLMs, LangChain, LangGraph, Agentic AI, RAG, orchestration frameworks, machine learning, SQL, and production AI operations.

Leads the stability, scalability, and improvement of production AI and GenAI applications. Responsibilities include machine learning development, production issue resolution, customer onboarding, release management, governance and compliance, stakeholder communication, automation, mentoring, and evaluation of emerging AI technologies.

Own the vision, roadmap, and backlog for a data intelligence platform supporting payments reconciliation and AI/ML use cases. Translate complex financial requirements into prioritized engineering work, coordinate multiple data engineering teams, define acceptance criteria, and remove blockers. Partner with operations and payments stakeholders on controls, audit needs, and product capabilities. Track data quality and pipeline metrics, make decisions on reconciliation automation, and promote strong engineering standards, observability, and reliable delivery.

Design, build, and evolve secure, scalable full-stack sales applications incorporating generative AI, intelligent workflows, APIs, data services, and cloud infrastructure. Lead architecture and technical design decisions, integrate AI models and retrieval systems, improve testing, CI/CD, observability, and production reliability, and mentor engineers. Collaborate with Product, AI/ML, Data, Security, Solution Architecture, and Engineering teams to deliver enterprise applications globally.