Top Machine Learning Jobs in Pune

Develops and productionizes machine learning and data science solutions for Mastercard’s Business & Market Insights products. Responsibilities include feature engineering, model training and deployment, exploratory analysis, large-scale PySpark processing, advanced SQL, pipeline optimization, responsible AI controls, and product innovation. Partners with product, engineering, sales, and clients to prioritize solutions, deliver milestones, incorporate feedback, and improve products. Provides technical leadership and mentorship to junior data scientists.

Develops and productionizes machine learning and data science solutions for Mastercard’s Business & Market Insights products. Responsibilities include feature engineering, model training, evaluation, deployment, exploratory analysis, large-scale data processing with PySpark, advanced SQL, and scalable ML workflows. The role partners with product, engineering, client services, and sales teams to deliver analytics-driven products, evaluates AI/ML approaches, drives product improvements, and mentors junior data scientists.

Leads data science and AI/ML product development, designing, productionizing, and optimizing traditional machine learning and generative AI solutions. Builds LangChain and LangGraph applications, processes large datasets with PySpark and SQL, evaluates model quality and performance, and applies privacy, security, responsible AI, monitoring, and explainability controls. Partners cross-functionally on product strategy, mentors data scientists, establishes reusable technical practices, and drives releases and continuous product improvement.

Develop statistically effective algorithms and apply machine learning, hypothesis testing, data mining, and retrieval techniques to analyze complex datasets. Identify trends, create visualizations, communicate findings to clients and stakeholders, and evaluate emerging datasets and technologies. The role requires collaboration across consulting and analytics teams, with opportunities to build scalable solutions using generative AI, deep learning, big data, and cloud technologies.

Leads client-facing AI and digital transformation engagements from strategy through implementation and adoption. Manages cross-functional teams building enterprise Generative AI, Agentic AI, machine learning, and analytics solutions. Advises senior stakeholders, develops client relationships, supports business development, manages budgets and vendors, ensures engineering and architecture quality, hires and mentors talent, and drives AI scaling initiatives across consulting engagements.

Leads and develops a Data Science team, oversees predictive and machine learning model delivery, establishes governance and lifecycle standards, and partners with business, engineering, and analytics leaders. Translates complex data into executive insights, frames ambiguous business problems, improves analytical processes, ensures production-ready models, and measures business impact while developing technical talent and promoting responsible, explainable analytics.

Develop and scale globally relevant data analytics and agentic AI products for enterprise and credit risk customers. Partner with analytics, machine learning, engineering, operations, legal, customer experience, commercial, and product teams. Gather customer feedback, analyze data, define product direction, create requirements and user stories, communicate product updates and risks, and support go-to-market initiatives throughout the product lifecycle.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Leads technology risk, application security, AI governance, and compliance assessments across applications, cloud services, platforms, third parties, and AI solutions. Defines assessment scope, evaluates controls and residual risk, documents findings, oversees remediation, supports incidents, and provides management reporting. The role also contributes to security policies, assessment methodologies, DevSecOps governance, secure software development, AI risk management, and continuous improvement while collaborating with technical, legal, privacy, compliance, and business stakeholders.

Develop advanced data science algorithms, apply statistical and machine learning techniques to large datasets, evaluate emerging data technologies, and build scalable generative AI and deep learning solutions. Collaborate with clients and engineering teams to communicate findings, deploy models into production, and maintain reliable workflows using software engineering best practices.

Leads software engineering squads delivering scalable, high-performing products. Responsibilities include architecture and system design, full-stack development, prioritization, cross-functional coordination, mentoring, quality and security oversight, automation, testing, root-cause analysis, operational excellence, and continuous improvement across the software development lifecycle.

Build and scale machine learning systems for search, including ranking models, feature engineering, model deployment, monitoring, experimentation, and online inference. Design fault-tolerant distributed infrastructure, optimize search latency and throughput, and develop reliable ML services from proof of concept through production. Collaborate across engineering and product teams, communicate technical decisions, review code, document systems, and mentor junior engineers.

Designs data-driven digital products, prototypes, dashboards, applications, chatbots, and interfaces using human-centered design, Lean UX, and Design Thinking. Leads workshops, develops personas and user journeys, creates visual and interaction designs across web, mobile, and IoT, and collaborates with product, technology, marketing, and regional teams. Conducts iterative user testing, contributes to design systems, and shapes AI- and ML-enabled experiences while balancing usability, business goals, regulatory compliance, and global user needs.

Leads enterprise data quality strategy, architecture, platforms, frameworks, observability, governance, and automation. Designs reusable quality capabilities for batch, streaming, cloud, on-premises, and SaaS environments; establishes rules, metrics, alerts, and remediation workflows; integrates quality with metadata, lineage, catalogs, MDM, and data contracts; mentors engineering teams; evaluates tools; and applies AI and machine learning to anomaly detection, root-cause analysis, and trusted AI-ready data platforms.

Senior Product Manager responsible for defining and delivering shared services for SailPoint’s enterprise identity security platform. The role partners with Product, Design, Engineering, and stakeholders to guide SaaS and cloud capabilities from discovery through launch, prioritize roadmaps, define requirements, track adoption metrics, and shape long-term platform strategy. The position is hybrid in Pune and requires approximately 15% travel.

Leads development of secure, scalable software services and applications using Java, Spring, Kafka, APIs, and distributed-system patterns. Owns complex cross-service problems, architecture, prioritization, automation, integration, troubleshooting, incident analysis, and operational excellence. Mentors engineers, guides technical decisions, promotes secure coding and quality practices, partners with product teams, supports agile delivery, and conducts technical interviews.

Develop and evaluate machine learning and AI solutions, experiment with AI agents and agentic workflows, and help deploy AI-powered tools. Assess model quality, reliability, and user experience while collaborating with engineers, product teams, and subject matter experts. Present findings to stakeholders and gain hands-on experience with modern AI technologies and cloud platforms.

Develop and validate AI and machine learning models across credit risk, marketing, fraud, and account management domains. Use GCP to automate workflows, prepare training data, develop algorithms, and execute AI validation projects. Collaborate with stakeholders across geographies, support model risk management policies, ensure regulatory compliance, and apply industry best practices. The role requires advanced Python and SQL skills, model validation experience, and knowledge of financial risk models.

Leads customer behavior analytics for a pharmaceutical commercial organization, including HCP, account, patient, and consumer segmentation; propensity, risk-stratification, KOL, content-affinity, and campaign analytics. Manages a 15+ person team and cross-functional insights pods, translates commercial priorities into scalable analytical solutions, oversees real-world data analysis, and advances AI, ML, NLP, and GenAI experimentation to generate actionable insights and measurable business value.

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.

Designs, develops, implements, and maintains statistical and machine learning models for transaction monitoring, fraud detection, credit risk, customer segmentation, and marketing optimization. Responsibilities include data acquisition, preprocessing, data management, real-time model monitoring, bias detection, stakeholder advising, risk and controls management, and mentoring analytics specialists. The role also evaluates emerging AI technologies and guides strategic analytical initiatives.

Lead enterprise AI initiatives from discovery through deployment, translating business requirements into technical roadmaps and delivering scalable software solutions. Collaborate with product, engineering, and data science teams in agile environments, lead technical assignments, manage stakeholders, promote secure coding and testing practices, and navigate governance requirements in a regulated environment.

Develops and integrates radiating components and passive radiation-pattern structures for multiband base-station antennas. Responsibilities include 3D and 2D electromagnetic simulation, prototype RF measurements, performance optimization, troubleshooting, technical documentation, and supporting the product lifecycle through mass production. The role collaborates with international cross-functional teams and requires relocation to Rosenheim, Germany.

Provide production support for critical banking technology infrastructure, rapidly resolve incidents, participate in major incident management, and improve application-support efficiency. The role involves Linux/Unix administration, Java and database technologies, middleware, microservices, containers, cloud platforms, monitoring, scheduling, CI/CD, and messaging tools. Responsibilities also include stakeholder communication, risk and control management, technical issue resolution, operational support during shifts and releases, and occasional weekend coverage.

Lead end-to-end delivery of large, complex technical epics and engineering initiatives. Manage engineering teams, dependencies, risks, milestones, releases, governance, and stakeholder communications. Partner with engineering, architecture, product, business, and delivery teams to ensure predictable, high-quality outcomes. Facilitate Agile practices, resolve impediments, establish accountability, and translate technical information into business insights. Experience delivering AI, machine learning, cloud, data, API, or enterprise-scale initiatives is advantageous.