Top Machine Learning Jobs in Pune

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.

Leads end-to-end delivery of complex technical epics and engineering initiatives across multiple teams. Responsibilities include planning, estimation, dependency and risk management, Agile delivery, governance, stakeholder communication, milestone tracking, and executive reporting. The role requires strong software development lifecycle knowledge, engineering delivery experience, and the ability to translate technical issues into business outcomes. Experience with AI, Generative AI, Machine Learning, cloud, data, API, or enterprise-scale initiatives is advantageous.

Lead full-cycle recruiting for complex engineering roles across distributed systems, cloud, security, data, and AI/ML in India. Advise engineering leaders on leveling, team design, location strategy, and market intelligence. Build diverse sourcing pipelines, conduct technically credible screens, close senior candidates, improve hiring processes, and lead talent acquisition initiatives. Mentor peers and establish recruiting standards for technical hiring.

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.

Lead end-to-end delivery of production-grade ML, Generative AI, and RAG solutions across multiple pods. Develop architecture-aligned delivery plans, manage dependencies, risks, releases, NFRs, and production readiness, while representing teams in senior governance forums. Guide ML versus GenAI decisions, RAG implementation, evaluation, guardrails, security, monitoring, and responsible model usage. Translate technical requirements into actionable epics and stories and ensure measurable business outcomes.

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.

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.

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.

Leads the sales organization by setting strategy, managing targets, recruiting and developing sales staff, building strategic client and partner relationships, monitoring performance, forecasting revenue, managing budgets, and collaborating cross-functionally to drive market expansion and business growth.

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.

Analyze large datasets, investigate data quality and lineage issues, build data pipelines, apply statistical and machine learning techniques, and create dashboards and reports. Translate findings into actionable recommendations for operational improvement, risk mitigation, and strategic decision-making. Partner with business, technology, risk, finance, operations, and data teams while ensuring analytical outputs meet governance, privacy, security, regulatory, and control standards. The role may lead analytical workstreams or teams and influence senior stakeholders.

Develop novel AI and machine learning algorithms and production-ready applications for enterprise challenges. Work across computer vision, NLP, speech, multimodal, generative, and agentic AI; collaborate with responsible AI, delivery, and academic teams; publish research; develop real-world assistants and automation solutions; and mentor AI talent.

Analyze business challenges and identify AI, automation, and digital transformation opportunities. Perform exploratory data analysis, build AI prototypes and automations, write and test Python, queries, prompts, and workflow logic, and collaborate with engineers, data scientists, and stakeholders. Translate requirements into solution plans, evaluate emerging AI technologies, and deliver practical AI applications, agents, copilots, and proof-of-concepts.

Leads data science teams supporting commercial healthcare analytics, translating business needs into analytical problems and production-grade ML and GenAI solutions. Responsibilities include defining technical requirements, developing models, applying GenAI, managing end-to-end projects, collaborating with stakeholders, presenting insights to executives, mentoring data scientists, and building expertise in oncology analytics, patient-level data, HCP analytics, and segmentation.

Own and improve data governance for customer due diligence and financial crime domains. Analyze data lineage, quality, controls, and authoritative sources; design pipelines and logical data models; apply advanced analytics and AI; and create dashboards and automated reports. Translate regulatory and EDD requirements into data rules, insights, and actionable recommendations while advising stakeholders and strengthening risk controls.

Designs, develops, and operates enterprise automation and AI-enabled solutions using ServiceNow, Power Platform, machine learning, and agentic AI. Builds workflows, portals, applications, integrations, and CSM solutions; supports platform modernization, governance, security, testing, and production stability. Provides technical leadership, troubleshooting, vendor coordination, stakeholder collaboration, and team coaching while driving adoption of emerging automation technologies.

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.

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.

Develop and deliver pharmaceutical forecasts and analytical solutions using statistical and ML techniques. Collaborate cross-functionally to build assumptions, automate and improve forecasting processes, communicate findings to stakeholders, and maintain documentation and forecasting tools.

Build and maintain data architectures, pipelines, warehouses, and lakes that ensure accurate, accessible, secure, and reliable data. Develop processing and analysis algorithms for complex, high-volume data and collaborate with data scientists to deploy machine learning models. Set technical strategy, advise senior stakeholders, manage risks and controls, guide technical direction, lead teams or specialist assignments, and drive continuous improvements aligned with business objectives.

Design, develop, and optimize secure, scalable cloud data platforms and ETL/ELT pipelines across AWS, Azure, and GCP. Write Python and SQL, implement data governance and quality controls, optimize performance and costs, and support analytics and BI layers. Contribute to architecture, pre-sales demos, proofs of concept, and client discussions. Mentor junior developers, conduct code reviews, and collaborate with architects, QA, DevOps, and business stakeholders.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

Leads technical onboarding, enablement, and co-selling support for contact center partners. Designs scalable Zendesk, Amazon Connect, and AWS architectures; provides technical strategy, mentorship, solution guidance, demonstrations, proofs of concept, and implementation support. Develops partner training materials, ensures security and governance compliance, and promotes AI, machine learning, automation, and omnichannel contact center innovations. Collaborates with sales, engineering, vendors, and internal teams to drive partner success and accelerate solution deployment.

PhD intern will research and prototype AI techniques for Azure SQL Database, analyze algorithms on large datasets, build scalable ML systems, collaborate with researchers and product teams, and contribute to technology transfer and publications.