Top Machine Learning Jobs in India

Develop, enhance, test, and support scalable Java full-stack applications and distributed systems. Create technical designs, lead architecture and code reviews, improve existing codebases, and troubleshoot complex issues. Work with cloud infrastructure, DevOps, databases, big data, security, testing, and AI modernization initiatives. Collaborate with cross-functional teams, provide technical guidance, and mentor developers while driving high-quality delivery and independent decision-making.

Develop and enhance scalable Java full-stack software solutions, including web services and distributed systems. Responsibilities include technical design, coding, testing, debugging, code reviews, architecture reviews, documentation, system quality improvements, and technical support. The role leads architecture and implementation efforts, mentors developers, collaborates cross-functionally, and works with cloud infrastructure, CI/CD, big data, AI modernization, and security practices.

Leads the design and production deployment of advanced machine learning solutions across NLP, machine perception, and Generative AI. Establishes technical vision, develops and optimizes neural network architectures, automates ML workflows, architects data pipelines, and drives scalable model integration, lifecycle management, and continuous improvement. Mentors engineers and balances model quality, reliability, latency, scalability, and cost in production environments.

Lead data and analytics strategy for Global Stores, defining architecture and roadmaps, delivering scalable data platforms and AI solutions (Gen AI/Agentic AI), driving cross-functional delivery, ensuring data quality/security, and mentoring engineering teams.

Leads enterprise detection engineering across SIEM, EDR, NDR, cloud, on-premises, and OT/ICS environments. Owns detection strategy, content development, MITRE ATT&CK coverage, platform tuning, automation, detection-as-code, quality standards, and performance metrics. Partners with SOC, incident response, threat intelligence, offensive security, engineering teams, and vendors to validate detections, reduce false positives, close coverage gaps, and improve time-to-detect.

Leads technical partnerships with system integrators and cloud providers, developing generative AI solutions, reference architectures, and deployment best practices. Supports strategic customer deals, enables partner AI practices, troubleshoots complex issues, presents at technical events, gathers product feedback, and informs Anthropic’s roadmap and go-to-market strategy.

Develops hardware-accelerated AI software for multimedia platforms, including audio, video, augmented reality, computer vision, and streaming applications. Responsibilities include designing algorithms, deploying applications across platforms, profiling and optimizing AI pipelines, addressing system-level resource challenges, and writing Python and C++ code. Requires substantial Linux software development experience, cloud-native architecture knowledge, and expertise in computer vision, machine learning, and deep learning.

Design and scale observability and reliability frameworks using metrics, logs, traces, predictive analytics, anomaly detection, and machine learning. Establish dashboards, SLAs, SLOs, SLIs, and high-availability architecture for distributed systems. Develop AIOps capabilities for root-cause analysis, predictive capacity planning, alert deduplication, and noise reduction. Mentor engineering teams and communicate complex technical concepts to leadership.

Develop and deploy production-ready AI and machine learning applications using cloud AI services, generative AI, deep learning, neural networks, chatbots, and image processing. Lead model development, coordinate cross-functional teams, make technical decisions, mentor junior engineers, and ensure scalable, reliable solutions aligned with organizational objectives.

Leads global Quality, Operations, and Continuous Improvement platforms, analytics, AI transformation, governance, and operational excellence. Owns product roadmaps, Agile delivery, platform adoption, business cases, ROI, stakeholder alignment, and enterprise-scale improvement initiatives across manufacturing and business units.

Owns account management, revenue targets, client relationships, and media growth for key advertising accounts in India. Drives cross-selling, upselling, campaign optimization, issue resolution, and strategic client conversations. Coordinates Sales, Product, Ad Operations, Engineering, Finance, Legal, Marketing, and other teams while providing data-driven insights and feedback on product and market needs.

Leads omnichannel customer engagement measurement and advanced analytics across pharmaceutical HCP and patient touchpoints. Performs attribution, causal inference, machine learning, experimentation, dashboarding, and campaign performance analysis. Translates analytical findings into commercial recommendations, partners with cross-functional stakeholders, mentors analysts, and develops reproducible, governed measurement frameworks and automated workflows.

Develop, maintain, enhance, and test software components across client applications. Analyze requirements, design solutions, implement machine learning models, preprocess data, engineer features, evaluate models, and optimize solutions for real-world use. Collaborate with cross-functional teams, contribute to technical discussions, resolve problems, ensure timely delivery, improve application performance, and mentor junior team members.

Lead requirements engineering and process mapping for a clinical imaging platform supporting ingestion, validation, curation, analysis, visualization, search, and secure sharing. Translate biomedical research workflows into functional specifications, user stories, wireframes, and data-flow diagrams. Define DICOM/NIFTI metadata standards, FAIR data governance, automated QC, ML dataset management, provenance tracking, federated learning, and integrations with hospitals, academic institutions, and CROs. Collaborate across researchers, data scientists, clinical operations, and engineering teams.

Leads and develops a high-performing engineering team building scalable, reliable AI-driven growth and trust-and-safety platforms. Responsibilities include setting technical strategy, overseeing software delivery, partnering cross-functionally, hiring and mentoring engineers, managing stakeholder communication, and ensuring operational excellence, reliability, scalability, and incident readiness. Requires extensive engineering and management experience, strong technical leadership, and proficiency in a major programming language.

Leads global Finance Transformation programs, including AI and automation initiatives from use-case identification through deployment and scaling. Responsibilities include program planning, governance, budgeting, risk management, stakeholder communication, financial reporting, AI governance, compliance, change management, and benefits realization. Partners with Finance, Technology, Data, Procurement, Legal, Risk, vendors, and senior leadership to deliver measurable improvements across finance processes.

Build modular, API-first internal product creation tools for Nike using cloud-native software engineering. Develop modern web applications and backend services, collaborate with product managers and principal engineers, apply architectural patterns, testing practices, CI/CD, infrastructure as code, AWS services, and frontend technologies. Support scalable tools for Nike’s Innovation and Consumer Creation teams, with opportunities to integrate AI and machine learning solutions.

Lead large-scale enterprise AI transformation programs from strategy through implementation. Responsibilities include defining delivery roadmaps and governance, managing global cross-functional teams, overseeing budgets and risks, partnering with executives and clients, supporting proposals, driving Responsible AI adoption, and mentoring delivery leaders. The role requires extensive experience delivering AI, Generative AI, Agentic AI, Machine Learning, and AI/ML platform solutions across cloud environments.

Own the technical roadmap for Material Master data stewardship platforms, including Syniti DSP integrations with SAP ECC and SAP S/4HANA. Lead data governance, workflow redesign, automation, AI adoption, release management, GxP compliance, validation, audit traceability, and global stakeholder collaboration. Translate business requirements into technical specifications, prioritize backlogs, oversee UAT, and mentor teams in automation and digital practices.

Leads technology and AI strategy for a client account, developing roadmaps, governance frameworks, predictive models, intelligent dashboards, and data-driven performance management. Oversees BI, data science, data quality, reporting automation, and AI solution deployment. Advises clients and internal stakeholders, manages complex technology initiatives, and drives measurable improvements in efficiency, cost savings, predictive accuracy, and client satisfaction.

Lead enterprise AI initiatives from discovery through deployment, translating business requirements into technical roadmaps and delivering scalable software solutions. Collaborate with product, engineering, and data science teams in agile environments, lead technical assignments, manage stakeholders, promote secure coding and testing practices, and navigate governance requirements in a regulated environment.

Design and develop production-ready AI and machine learning applications using cloud AI services, on-premises pipelines, generative AI, deep learning, neural networks, chatbots, and image processing. Lead architecture and integration of emerging AI technologies, optimize pipelines for reliability and scalability, advise multiple teams, translate business requirements into AI solutions, and mentor junior engineers.

Designs and develops production-ready AI and machine learning applications using cloud and on-premises pipelines. Integrates generative AI, deep learning, neural networks, chatbots, and image processing into scalable solutions. Leads emerging technology evaluation, optimizes AI pipelines, advises multiple teams, translates business requirements into AI solutions, and mentors junior engineers.

Design and develop production-ready AI and machine learning applications using cloud and on-premises pipelines. Integrate generative AI models, deep learning frameworks, neural networks, chatbots, and image processing capabilities. Lead evaluation of emerging AI technologies, optimize AI pipelines, advise multiple teams, translate business requirements into solutions, make technical decisions, and mentor junior engineers.

Designs and builds custom AI infrastructure and hardware solutions for large-scale machine learning workloads. Optimizes computational stacks for performance, power consumption, cost, and scalability; evaluates vendors and integrates full technology stacks. Leads infrastructure strategy, cross-functional coordination, technical decisions, team mentorship, and knowledge sharing.