Top Machine Learning Jobs in Pune

Designs and develops content governance platforms, anti-abuse tools, rule engines, and scalable governance pipelines. Integrates recommendation systems, implements content takedown policies, and builds capabilities for content review, risk detection, policy enforcement, and effectiveness analysis. The role addresses spam, fraud, traffic diversion, low-quality content, and black- or gray-market activity while collaborating with product, operations, algorithm, and business teams.

Design, build, and evolve secure, scalable full-stack sales applications incorporating generative AI, intelligent workflows, APIs, data services, and cloud infrastructure. Lead architecture and technical design decisions, integrate AI models and retrieval systems, improve testing, CI/CD, observability, and production reliability, and mentor engineers. Collaborate with Product, AI/ML, Data, Security, Solution Architecture, and Engineering teams to deliver enterprise applications globally.

Drive adoption of Databricks Genie across a regional territory through technical strategy, executive engagement, workshops, presentations, demos, and customer-facing collateral. Partner with Account Executives, GTM, Product, Engineering, GSIs, and consulting firms. Architect governed analytics solutions spanning semantic models, warehouses, metric layers, Lakehouse, and AI ecosystems; address governance and security concerns; resolve complex technical escalations; and provide roadmap feedback.

Leads Oncology commercial analytics and brand strategy insights across inline and pipeline assets. Translates business priorities into analytical plans, oversees patient journey and real-world data modeling, supports precision medicine and biomarker initiatives, and applies statistical, machine learning, and AI methods. Manages 15+ team members and cross-functional insights pods while partnering with global, regional, marketing, clinical, medical affairs, and business development stakeholders. Drives scalable analytics, GenAI experimentation, prioritization, value tracking, and executive decision-making.

Manages scientific manuscripts from submission through final decision, including scope and quality screening, peer-review coordination, editorial decisions, integrity investigations, revised manuscript assessment, and author communication. The role also commissions articles and special issues, manages contributor pipelines, analyzes editorial performance data, recommends alternative journals, and collaborates with global editorial teams. A relevant PhD is required, with expertise in areas such as AI, robotics, IoT, sensors, or smart cities.

Leads analysis, mining, modeling, and visualization of large-scale transactional, operational, and customer data. Partners with compliance teams to prioritize analytical use cases, support regulatory requirements, improve business efficiency, and translate complex findings into actionable recommendations. Oversees multidisciplinary analysts, manages stakeholder data requests, assesses business changes, and develops analytical systems using machine learning, big data technologies, and visualization tools.

Lead the architecture and development of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic AI systems for intelligent networking. Drive the full software lifecycle from design through production operations, establish technical direction, mentor engineers, and improve system reliability, security, observability, performance, and scalability.

Lead the technical vision, architecture, development, testing, deployment, and operations of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Build secure, resilient, observable services processing high-volume network data; guide technical strategy, improve engineering practices, and mentor engineers. Translate complex technical challenges into production-ready solutions across intelligent networking, cloud platforms, analytics, and automation.

Lead the architecture, development, testing, deployment, and operation of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Define technical vision, build secure and observable high-performance services, process large-scale network data, and integrate intelligent networking capabilities. Mentor engineers, lead technical discussions and reviews, improve system reliability and performance, and translate complex technical challenges into production-ready solutions.

Lead the architecture and development of scalable, secure, observable AI infrastructure, distributed microservices, real-time data pipelines, and agentic systems for intelligent networking. Drive technical vision, software delivery, system reliability, and operational excellence while mentoring engineers. Build production platforms processing high-volume network and operational data, integrating cloud infrastructure, machine learning, telemetry, and automation capabilities.

Own enterprise pipeline generation for strategic accounts across BFSI, manufacturing, and government. Design account-based outbound campaigns, conduct consultative discovery with C-level buyers, maintain CRM data quality, track conversion metrics, and partner with Account Executives on account planning. Mentor junior BDRs, support playbook development, and engage prospects around Rubiscape’s unified data, BI, ML, and AI platform.

Develop and deploy generative AI applications, integrate LLM APIs, perform text analysis using traditional machine learning and GenAI, and manage the machine learning lifecycle from problem definition through deployment and monitoring. Collaborate with cross-functional teams, translate business needs into technical solutions, evaluate new GenAI techniques, and document technical concepts.

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.

Advise clients on developing and implementing data governance strategies. Design policies, frameworks, and practices for data quality, privacy, metadata management, and security. Deliver governance projects, build capability with frameworks and accelerators, and promote governance awareness across stakeholders and committees.

Design and implement a unified orchestration layer coordinating vendor and internal LLMs and human agents. Build production-grade Python services, drive ML projects from design to delivery, ensure secure, scalable AI-enabled customer experiences, and collaborate across teams to meet security, compliance, and performance standards.

Define and govern Sensata’s enterprise AI architecture while remaining hands-on in designing, prototyping, and guiding production AI systems. Develop reference architectures, data and model lifecycle standards, GenAI guardrails, RAG and agent patterns, integration approaches, evaluation and observability practices, and reusable implementation assets. Partner with engineering, data, security, legal, compliance, and business teams; evaluate vendors and build-versus-buy options; prioritize use cases; and provide technical leadership for scalable, responsible AI adoption.

Manage strategic enterprise customer relationships, serving as the primary contact for technical support, training, issue resolution, and executive engagement. Partner with sales on account health, growth, and presales activities while advocating for customer needs internally. Apply data analytics, visualization, AI tools, and automation to improve reporting, insights, troubleshooting, and customer value. Support connectivity-focused products and regularly meet clients and attend company functions.

Provide mid-level technical support by troubleshooting and resolving customer issues, managing support tickets to meet SLAs, collaborating with cross-functional teams, improving support processes, and delivering training to junior staff. Support multiple Zebra products and use AI-driven tools to diagnose and resolve problems.

Provide intermediate-level technical support for multiple Zebra products: troubleshoot and resolve moderate technical issues, manage support tickets within SLAs, collaborate cross-functionally, contribute to process improvements, and train entry-level team members.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Leads finance transformation by owning core financial processes, modernizing ERP and planning systems, deploying AI and automation, partnering with engineering and data teams, redesigning workflows, establishing controls, and driving organizational adoption. The role requires senior finance leadership, strong systems expertise, hands-on AI transformation experience, process governance, and the ability to translate between finance and technology.

Support product development for homepage, user center, AI-generated interfaces, market pages, onboarding, personalization, and recommendation experiences. Coordinate with engineering, design, data, and business teams; organize requirements; track metrics; support experiments; analyze feedback; and help resolve issues. The role is part of a 3–6 month early-career accelerator program focused on blockchain products and AI-driven user experiences.

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.

Lead requirements gathering, process analysis, and documentation for Legal IT systems. Support design, testing, training, stakeholder communication, and project delivery. Identify automation/AI opportunities and monitor KPIs to improve contract lifecycle efficiency and compliance.

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.