Top Machine Learning Jobs in Pune

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 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.

Develops and analyzes mechanical and thermal systems through dynamic and steady-state simulation. Writes system and simulation requirements, contributes to architecture, integration, controller design, and real-time measurement and control. Uses MATLAB, Simulink, and Simscape to model aerospace products, including valves, heat exchangers, air conditioning, and refrigeration systems. Collaborates globally, supports R&D and new product development, and applies analytical problem-solving to conceptualize and validate simulation models.

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.

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.

Reviews and audits electronically submitted legal invoices for compliance with billing guidelines, verifies UTBMS codes, adjusts improper line items, and documents decisions. The Consultant provides legal billing guidance, supports quality assurance and reconsideration reviews, analyzes performance data, helps develop procedures, trains analysts, and collaborates with technology teams to improve machine-learning tools. The role requires strong legal terminology knowledge, civil procedure familiarity, analytical judgment, communication, organization, and legal bill review experience.

Designs, develops, and deploys scalable data science, machine learning, generative AI, and agentic AI solutions. Translates business needs into secure enterprise architectures, builds and evaluates production models, leads AI initiatives from concept through deployment, and establishes monitoring and performance practices. Collaborates with product, engineering, data, and business stakeholders while providing technical leadership across conversational AI, intelligent agents, retrieval systems, and advanced analytics initiatives.

Analyze and improve data quality, lineage, controls, and authoritative sources. Build data pipelines, logical data models, dashboards, reports, and automated analytics. Apply statistical, machine learning, and advanced analytical techniques to identify trends, risks, anomalies, and improvement opportunities. Translate complex findings into actionable recommendations for senior stakeholders while ensuring data governance, privacy, security, regulatory compliance, and reporting accuracy. Partner across business, technology, risk, finance, operations, and data teams, potentially leading analytical workstreams.

Design, build, and maintain data pipelines, data warehouses and lakes; implement data processing and analysis algorithms; collaborate with data scientists to deploy ML models; lead performance engineering, system stability and scalability efforts; partner with cross-functional teams and ensure compliance with controls and service-level expectations.

Owns a cybersecurity detection coverage area, including backlog prioritization, technical direction, detector design, implementation, code reviews, tuning, and performance measurement. The role improves detection fidelity, ATT&CK coverage, false-positive reduction, and regression prevention while translating threat intelligence, threat hunting, and SOC feedback into roadmap priorities. It requires deep expertise in at least one detection domain and hands-on detector-as-code development with version control and controlled deployment.

Designs, develops, deploys, and optimizes NLP chatbots, virtual assistants, and generative AI solutions. Responsibilities include selecting and training language models, preprocessing data, validating and fine-tuning models, integrating solutions with enterprise platforms, monitoring performance, and collaborating with technical and business teams. The role also establishes AI development standards, supports autonomous agents, and evaluates emerging AI technologies.

Leads mainframe modernization and migration initiatives for large global clients. Develops transformation and re-platforming solutions, conducts discovery workshops, presents technical and business cases, advises executives, and partners with sales, consulting, operations, and product teams. Requires extensive mainframe expertise across legacy technologies, migration architecture, data modernization, cloud and hybrid environments, and enterprise hosting. The role also supports AI-driven modernization, technical documentation, project lifecycle management, and client relationship development.

Builds and optimizes AI Operations solutions for IT processes, including incident management, root cause analysis, monitoring, anomaly detection, and infrastructure problem resolution. Develops and deploys machine learning models, AI agents, and CI/CD pipelines while integrating AI solutions with existing infrastructure. Collaborates with data science, software engineering, and operations teams to improve reliability, scalability, performance, and operational efficiency.

Build and support secure full-stack web applications, internal tools, APIs, data pipelines, and database schemas. Develop AI agents and LLM-powered solutions using Agentic AI, RAG, tool calling, guardrails, and Microsoft Foundry. Deploy machine learning models for predictive analytics, implement automated testing and CI/CD, resolve application and data issues, and modernize legacy systems. Collaborate with technical teams and business stakeholders to deliver scalable information and decision-support solutions.

Owns Sensata’s commercial AI product portfolio across sales, revenue operations, pricing, marketing, and finance. Maps workflows, identifies pain points, defines product requirements, prioritizes roadmaps, and manages backlogs. Leads pilots through production, user adoption, governance, vendor integration, and value measurement. Builds business cases, tracks revenue and productivity outcomes, and communicates product decisions to senior leaders while partnering with AI engineers, data scientists, IT, Finance, and business stakeholders.

Conduct security research involving malware, threats, vulnerabilities, adversary tracking, threat intelligence, detection, remediation, and reverse engineering. Analyze threat trends, process intelligence reports, identify security defects, contribute code and automation, support incident response, and use research results to improve security products and services. The role focuses on cloud-scale threat detection using machine learning and behavioral profiling.