Top Machine Learning Jobs in Pune

Leads product strategy and roadmap for GoDaddy’s account risk management capabilities. Defines platform and internal tool requirements, partners with engineering, design, business, and operations teams, identifies risk and performance gaps, and drives scalable solutions that balance fraud prevention, business growth, and customer experience. The role also requires senior stakeholder engagement and technical expertise in web services, APIs, AI, and anomaly detection.

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.

Build and productionize machine learning, generative AI, LLM, and automation solutions. Design industrialized processing pipelines, implement MLOps and LLMOps best practices, develop enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry, and integrate Microsoft and Azure services. Gather requirements, estimate work, present technical solutions, troubleshoot agents, and create documentation.

Lead AI and machine learning engineering activities for customers, partners, and prospects. Advise on GenAI/ML solutions, architect end-to-end workflows from data ingestion through deployment, lead proofs of concept, support customer delivery, and manage and mentor a team of AI and machine learning engineers. The role also involves pre-sales support, consultative solution design, technical presentations, and stakeholder engagement.

Associate Product Manager responsible for understanding customer needs, defining product opportunities, creating requirements and roadmaps, partnering with Engineering and Design, running experiments, analyzing metrics, and improving lending product outcomes. The role collaborates with Risk, Operations, Data Science, Compliance, and external partners while independently owning product initiatives and contributing to long-term product strategy.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

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.

Gather and define accounts payable and payments requirements across the invoice-to-pay lifecycle. Translate business needs into user stories, process flows, functional specifications, and acceptance criteria. Coordinate with stakeholders, engineering, architecture, QA, and delivery teams on ERP integrations, APIs, workflows, testing, compliance, reconciliation, and payment processing. Support prioritization, product delivery, automation opportunities, and platform modernization initiatives.

Provide advanced technical support for escalated customer issues across email, phone, and chat. Troubleshoot Zebra products, collaborate with engineering and product teams, document resolutions, create knowledge base content, analyze support trends, and improve service processes. Train and mentor junior team members, maintain stakeholder relationships, and stay current with emerging technologies and industry practices.

Lead the design, development, deployment, and optimization of production-grade Generative AI systems. Build RAG pipelines, fine-tune foundation models, develop AI agents, establish evaluation and observability frameworks, and integrate multimodal AI capabilities. Collaborate with product, engineering, data, and business stakeholders to deliver scalable, reliable, secure, and responsible AI solutions.

Lead Workday integrations, packaged application implementations, custom Workday Extend app development, and cross-functional product launches. Configure Workday Studio, EIB, and Core Connector solutions; manage application lifecycles through production deployment; establish platform best practices; and support adoption through team development. Apply HR, payroll, and compensation domain knowledge while exploring AI, machine learning, and intelligent automation capabilities.

Leads Workday integrations and packaged application implementations, including customization, module implementation, enterprise application integration, and development using Workday Studio, EIB, Core Connector, and Workday Extend. Builds and deploys custom Workday applications, coordinates cross-functional product launches, establishes platform best practices, and supports adoption. Applies expertise in HR, payroll, and compensation while exploring AI, machine learning, and intelligent automation capabilities.

Supports sales-force effectiveness through data cleaning, access governance, KPI reporting, performance trend analysis, and migration from Excel tracking to Power BI dashboards. The role also provides user support, onboarding and training assistance, documentation, process standardization, and continuous improvement across regional sales entities.

Design and deliver scalable Generative AI solutions using LLMs, RAG, AI agents, vector databases, and multimodal models. Lead cloud, MLOps, security, governance, and architecture strategies across AWS, Azure, and Google Cloud. Translate business needs into technical solutions, support client workshops and pre-sales, present strategies to executives, and mentor data science, ML engineering, and software teams.

Conduct experimental research in machine intelligence, develop and refine machine learning algorithms and models, apply research findings to existing AI systems, collaborate with engineers and researchers, publish results, and stay current with advances in machine learning.

Develop statistical and machine learning models to optimize Akamai’s global mapping service. Analyze massive telemetry streams, build predictive observability tools and automated data pipelines, define reliability metrics, investigate outages, and guide architecture and technology investment decisions. Collaborate with product engineers and communicate complex quantitative insights to technical and executive stakeholders.

Owns end-to-end IT services presales, including opportunity qualification, solution development, bid management, RFP responses, proposals, presentations, pricing, and deal closure. Partners with sales, delivery, architects, product managers, and subject matter experts to create feasible, differentiated solutions. Conducts market and competitive analysis, supports go-to-market strategies, and develops value propositions across product engineering, AI, cloud, and data services.

Reviews financial services regulations and exchange rulebooks, assesses regulatory changes, and maintains auditable review records. Validates machine-learning topic classifications, provides labelled feedback, and helps maintain regulatory taxonomies. Compares platform rulebooks with authoritative sources to identify structural errors, missing provisions, duplication, and versioning issues. Performs quality assurance against development requirements and collaborates with product, engineering, and software teams to process new regulations and improve the service.

Leads biomedical data science for clinical and health research, integrating clinical, observational, EHR, genetic, wearable, electrophysiological, and video data. Develops signal-processing methods, machine-learning models, probabilistic and time-series analyses, scalable data pipelines, and reproducible workflows. Validates clinically relevant models, supports disease phenotyping and predictive modeling, ensures compliance with pharmaceutical and healthcare standards, and communicates technical findings to clients and stakeholders.

Build data infrastructure and reliable pipelines, analyze product usage, train traditional and LLM-based models, define product metrics, run experiments, and translate findings into product and go-to-market decisions. The role combines data science, data engineering, product analytics, machine learning, and stakeholder communication, with ownership across data modeling, evaluation, experimentation, and product insight generation.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop, train, evaluate, and deploy small task-specific ML models for real-time English conversational skills (ASR, pronunciation, grammar, recommendations). Create datasets and task-specific evaluations, improve internal SOTA, mentor engineers, and contribute to hiring.

Leads an integrated data, AI, analytics, and product engineering organization. Owns strategy, roadmaps, architecture, technical standards, production service levels, AI governance, client relationships, budgets, hiring, and organizational growth. Directs delivery of data platforms, dashboards, machine learning models, generative AI solutions, and full-stack applications while ensuring measurable business value, operational stability, responsible AI practices, and strong stakeholder governance.