Top Machine Learning Jobs in Pune

Designs data-driven digital products, dashboards, applications, chatbots, voice interfaces, and assistants using human-centered design, Lean UX, Design Thinking, and rapid prototyping. Leads workshops, develops personas and user journeys, creates interaction and visual designs, conducts iterative user testing, and shapes design systems across web, mobile, and IoT. Collaborates with product, technology, brand, marketing, regional, and customer teams to deliver accessible, usable, and compelling experiences.

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 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 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.

Analyze large datasets to identify trends, correlations, data-quality issues, and improvement opportunities. Build data pipelines, logical data models, dashboards, visual reports, and automated reporting processes. Translate analytical findings into business recommendations, support risk and control activities, and collaborate with stakeholders. The role also requires payments-domain knowledge, software development lifecycle delivery, and familiarity with cloud, CI/CD, machine learning, and AI concepts.

The Staff Machine Learning Engineer will architect and implement scalable GenAI platform solutions, ensuring reliability and safe AI experiences across product lines while mentoring engineers and leading technical strategies.

Analyzes large datasets to identify trends, correlations, data-quality issues, and actionable business insights. Builds data pipelines, logical data models, interactive dashboards, visual reports, and automated reporting processes. Partners with stakeholders to recommend operational improvements, strengthen controls, and support data-driven decisions. The role also involves data integration, engineering, analytics, machine learning fundamentals, and payments-domain knowledge.

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.

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.

Designs and deploys AI, Generative AI, intelligent automation, and process intelligence solutions to automate business processes and improve operational efficiency. Identifies automation opportunities, integrates AI with enterprise systems, analyzes processes using process mining, and delivers scalable, secure solutions. Collaborates across functions to translate business needs into measurable outcomes, while potentially leading workstreams, mentoring colleagues, shaping automation roadmaps, and supporting organizational AI transformation.

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.

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.

Provides technical consulting and lifecycle services for Cisco networking customers. Responsibilities include Day-2 support, routing and switching troubleshooting, SDA/ACI architecture and optimization, customer workshops, technical documentation, solution adoption, and proactive problem resolution. The role also develops automation and AI-enabled delivery assets, mentors engineers, coordinates cross-functional teams, and advises customers and executives on network strategy, resilience, and security.

Designs, experiments with, deploys, monitors, and evaluates machine learning models. Requires full-stack development involving Python, React, AWS, APIs, and SQL, along with statistical and probabilistic foundations. The role supports AI/ML initiatives in a consulting environment serving financial services and energy clients.

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.

Builds scalable ETL pipelines, cloud and data-lake integrations, and analytical tools supporting global supply chain planning and inventory optimization. Applies statistical methods, machine learning, AI-assisted development, and data visualization to portfolio segmentation, product lifecycle management, replenishment strategies, and decision support. Collaborates with stakeholders, leads complex data engineering projects, improves database performance, and promotes secure, governed, enterprise-wide data accessibility.

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.

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.

Design, develop, deploy, and operate secure full-stack applications with AI, machine learning, and large language models embedded as core capabilities. Build front ends, APIs, backend services, and data pipelines primarily in Python. Deliver cloud-native solutions across AWS, Azure, and hybrid environments while implementing security, scalability, testing, CI/CD, monitoring, and observability. Collaborate with business, technology, security, and operations teams to deliver enterprise-ready applications.

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.

Build and productionize LLM-powered agents, workflows, and RAG systems. Apply prompt engineering, embeddings, fine-tuning, and traditional ML to automate tasks and improve products. Mentor teammates and help design scalable, production-grade AI infrastructure while staying current on LLM advances.