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.

Designs, develops, tests, and maintains complex Java applications and production platforms. Responsibilities include implementing functionality, analyzing requirements, creating technical designs, writing high-performance code, automating unit and integration tests, reviewing changes, documenting APIs and decisions, supporting CI/CD and cloud deployments, resolving production issues, and contributing to future system architecture. The role also involves Agile collaboration, mentoring, knowledge sharing, and maintaining application stability and reliability.

Build and deploy AI and machine learning models for business challenges, develop Generative AI and Agentic AI solutions using LangChain and LangGraph, leverage LLMs, create data visualizations and actionable insights, and collaborate with cross-functional teams while managing data science projects.

Design, build, and maintain scalable data pipelines, warehouses and lakes; implement processing and analysis algorithms; support production deployments (DEV->UAT->PROD); ensure data quality, security, and governance; collaborate with data scientists to deploy ML models; lead or mentor team members and influence stakeholders to mitigate risk.

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.

Leads Financial Crime Technology application development, data architecture, AML and fraud-monitoring technology delivery. Oversees strategy, architecture, budgets, resources, risk, governance, system migrations, and regulatory compliance. Drives AI and generative AI innovation, including machine learning, LLMs, intelligent automation, explainable AI, and responsible-use guardrails. Partners with business, technology, legal, risk, and compliance leaders while managing and developing high-performing teams.

Create, update, and maintain high-quality technical documentation for PTC software products. Collaborate with product design and scrum teams to understand product changes, meet release schedules, apply documentation standards, and maintain consistent tone and voice. Analyze and implement SEO recommendations to improve user experience, participate in agile meetings, support department initiatives, and use AI-enabled tools to support the software development lifecycle.

Develop and deploy AI and machine learning solutions for DNV’s maritime business processes. The role covers stakeholder engagement, data analysis, technical architecture, experimentation, programming, testing, DevOps release cycles, and production integration. The successful candidate will identify new applied AI opportunities, collaborate with data scientists, developers, engineers, and domain experts, and contribute to business value through practical AI systems.

The role focuses on developing analytical data science products, implementing algorithms for insights, and expertise in deep learning frameworks.

Supports end-to-end technical project delivery, including process and equipment performance modeling, design documentation, customer workshops, testing, training, scheduling, and post-go-live value capture. Leads customer communication, manages expectations, addresses issues, and supervises and mentors team members. Requires expertise in industrial asset performance management, chemical process operations, reliability methodologies, predictive analytics, machine learning, SQL, system integration, and customized diagnostic model development.

Lead end-to-end engineering project delivery for asset performance management solutions in industrial process environments. Responsibilities include developing equipment and process performance models, preparing technical documentation, leading customer meetings and training, managing schedules and budgets, resolving issues, and monitoring post-launch value capture. The role also supervises team members and mentors junior colleagues. Required expertise includes process operations, equipment failure diagnosis, reliability methodologies, analytics, machine learning, SQL, system integrations, and APM platforms.

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.

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.

Conduct applied AI and machine learning research for Microsoft products. Design, train, fine-tune, and evaluate models including LLMs and SLMs; analyze large datasets; build scalable prototypes; design experiments and benchmarks; optimize model performance; and collaborate with researchers, engineers, and product teams to deploy research innovations. Document findings through technical reports, publications, patents, or open-source work.

Manages science and technology initiatives for sustainable industrial fisheries. Leads multidisciplinary projects involving electronic monitoring, AI, machine learning, computer vision, data systems, hardware, software deployment, and seafood traceability. Oversees vendors, contractors, budgets, procurements, technical assessments, and deployment of systems in remote environments. Translates technical concepts for diverse stakeholders and supports technology evaluation, scaling, and conservation strategy. The role may require domestic and international travel of 25% or more.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

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.

Build and validate production machine learning and statistical models for music and entertainment marketing. Develop real-time data systems, predictive scoring, trend detection, AI briefing classification, dashboards, and GenAI-assisted workflows. Collaborate with data engineering, paid media, and creative teams while implementing tokenization, anonymization, and cross-border data privacy controls. The role is a 12-month fixed-term contract supporting clients across APAC.