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.

Lead complex data science and analytics initiatives from concept through implementation. Develop predictive models, analytical frameworks, dashboards, and decision-support tools using statistical and machine learning techniques. Partner with stakeholders to translate business needs into scalable solutions, oversee data quality and integration, present findings, manage delivery, and mentor technical team members.

Own roadmap execution and product requirements for Mastercard’s global disputes platform. Manage the agile backlog, define epics and user stories, prioritize capabilities, and partner with engineering, architecture, data science, operations, and regional stakeholders. Balance global platform consistency with localized market needs, support AI-powered automation, facilitate discovery, guide releases, monitor outcomes, and drive adoption and continuous improvement across markets.

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.

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.

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.

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.

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.

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.

Develop and maintain DirectX graphics drivers for NVIDIA GPUs, develop graphics and AI-based rendering features, and build AI-agent automation for debugging, testing, triage, code analysis, documentation, and developer productivity. Collaborate across teams to deliver software objectives while improving quality and engineering workflows.

Build and deploy AI-powered products using machine learning, NLP, generative AI, RAG, and agentic AI. Develop prototypes, production solutions, reusable frameworks, APIs, and intelligent services while partnering with product, engineering, content, and business teams. Drive AI adoption, communicate business impact, mentor colleagues, and contribute to data science communities. The role requires strong Python, SQL, cloud, MLOps, software architecture, and data storytelling skills.

Deliver and support a real-time sanctions platform transformation using FircoSoft Continuity. Responsibilities include system integration, data architecture, MQ-based messaging, deployment and redundancy design, vendor coordination, data governance, and scalable solution delivery. The role also requires mentoring stakeholders, translating complex technical concepts, and ensuring auditable, testable implementations delivered on time and within budget.

Analyze product, customer, and business data to identify trends, risks, and opportunities. Build dashboards, metrics, reports, analytics solutions, and predictive models while supporting data pipelines and AI-powered automation. Partner with Product Managers, Engineers, customers, and business stakeholders to define requirements, improve workflows, validate product ideas, and measure outcomes. Own initiatives from problem identification through delivery while developing expertise in product management, data engineering, AI, and enterprise financial technology.

Lead data analysis and engineering to enable data-driven decisions: investigate data quality and lineage, build automated pipelines, apply advanced analytics and ML, design interactive dashboards and reports, translate insights into business recommendations, and support governance and controls while coaching or leading team members.

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.

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.

Perform advanced analytics on large datasets, design and build automated data pipelines, investigate data quality and lineage, and create interactive dashboards. Translate insights into actionable campaign and process recommendations, partner with stakeholders and support campaign delivery and BAU operations.

Lead the architecture and development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and AI lifecycle capabilities. Drive security, governance, observability, reliability, performance, and cost optimization while translating R&D concepts into production systems. Provide hands-on technical leadership, architecture reviews, mentorship, and cross-functional collaboration.

Lead the architecture and hands-on development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and ML lifecycle capabilities. Establish standards for security, governance, testing, CI/CD, observability, reliability, and cost optimization while providing technical leadership, mentoring, architecture reviews, and cross-functional collaboration.

Leads the architecture, development, and productionization of AI/ML and GenAI solutions for healthcare operations. Responsibilities include building clinical and claims data pipelines, developing LLM and RAG applications, implementing MLOps and LLMOps, ensuring HIPAA-compliant governance, translating business needs into technical roadmaps, communicating with stakeholders, and mentoring engineers.

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.

Leads the secure, governed distribution and integration of HR data across internal and external consumers. Establishes data governance, access controls, metadata, lineage, quality controls, and consumer engagement processes. Coordinates HR technology changes, impact assessments, data integration, transformation, migration, validation, reconciliation, and remediation. Partners with product, architecture, engineering, and HR teams on solution design, testing, implementation, operational readiness, and control management across complex transformation initiatives.

Lead design and implementation of ML tools for scenario classification, anomaly detection, sensor data quality, behavior analytics, and automated reporting. Build scalable pipelines and automated data curation for camera, radar, and LiDAR; create SIL/HIL replay interfaces, ground-truthing and annotation tools, and offline benchmarking. Integrate ML modules into simulation, HIL/SIL, and CI/CD ecosystems, implement reproducible workflows (Docker, CI/CD, artifact versioning, automated tests), monitor model/data quality and mentor junior engineers while collaborating with verification and perception teams.

Leads enterprise data architecture strategy and develops scalable data platforms, pipelines, warehouses, lakes, governance frameworks, and analytics solutions. Designs data models, integrations, migrations, and reporting architectures across AWS, Azure, Databricks, Snowflake, and BI platforms. Partners with AI/ML teams, business stakeholders, and senior leadership to modernize data capabilities, optimize performance, establish standards, and deliver complex analytical solutions. Mentors data engineers and architects while advancing data quality, security, compliance, and architectural governance.

Entry-level Full Stack Developer responsible for building responsive React.js interfaces, Python Flask or Django backend services, REST APIs, and database integrations. The role includes writing maintainable code, testing, debugging, code reviews, performance improvements, and collaboration with designers, product managers, and engineers. Candidates should have a relevant bachelor’s degree and 0–1 years of experience through internships, projects, or hackathons.