Top Machine Learning Jobs in India

Owns and evolves Moderna’s global SAP S/4HANA Accounts Receivable and cash application ecosystem. The role configures AR, credit management, lockbox, electronic bank statements, clearing, banking integrations, e-invoicing, and payment platforms. It translates business requirements into functional specifications, leads testing and rollouts, ensures SOX compliance, and drives automation using AI, machine learning, and RPA. The position also provides global training, documentation, and support across Finance, Treasury, and Digital teams.

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.

Develop advanced supply chain analytics solutions using descriptive, diagnostic, predictive, and prescriptive modeling, machine learning, statistical analysis, forecasting, and operations research. The role involves gathering requirements, leading projects, managing stakeholders, deploying and sustaining models with DevOps/MLOps practices, presenting insights, and mentoring analysts in R, Python, and supply chain analytics. The position requires working a 1:30 p.m. to 10:30 p.m. shift.

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.

Leads the design, development, deployment, and lifecycle management of machine-learning and advanced analytical models. Translates business problems into scalable data products, performs advanced exploratory analysis, supports productionization and monitoring, and applies forecasting, optimization, causal inference, and responsible AI practices. Collaborates with product owners, data engineers, and ML engineers while mentoring junior data scientists and promoting governance, reproducibility, and reusable AI capabilities.

Leads InMobi Group’s B2B thought leadership and corporate voice program. Defines the organization’s point of view, develops bylined and long-form content, supports executive speakers, secures media and industry opportunities, and coordinates contributors, agencies, and global stakeholders. The role also manages corporate voice consistency, develops internal experts, establishes performance KPIs, and uses analytics and social listening to improve reach, engagement, brand sentiment, and influence among talent, investors, technology professionals, and industry leaders.

Develop and maintain high-performance, scalable risk metrics engines and workflows. Write production-grade backend code, implement cloud-first architectures (AWS), integrate AI-enabled tools, follow CI/CD and testing best practices, and collaborate with quantitative engineers and product managers to support firm-wide risk, liquidity, and trading analytics.

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.

Develop and validate statistical and machine learning models under senior guidance. Perform data cleaning, feature engineering, exploratory analysis, hypothesis testing, model evaluation, and debugging. Support NLP and LLM experimentation, including prompt execution and output validation. Document models and experiments, follow coding standards, and collaborate with data scientists and engineers. The role requires a foundational understanding of statistics and machine learning, basic Python or R proficiency, and a bachelor’s degree in a relevant field.

Develop and maintain macOS endpoint security solutions using System Extensions and Apple security frameworks. Implement code signing and notarization, integrate endpoint protection with cloud services, and collaborate with architects on secure designs. Mentor junior developers, participate in code reviews, troubleshoot with macOS debugging tools, and stay current with platform security changes and endpoint threats.

Designs scalable, secure, cloud-native solutions spanning AI, data, full-stack, and enterprise application architecture. Translates business requirements into technology stacks, integration patterns, APIs, and data architectures. Provides technical leadership, reviews designs, supports proofs of concept, and collaborates with engineering, sales, and presales teams on proposals and client presentations. Requires expertise in generative AI, LLMs, data engineering, cloud platforms, full-stack development, distributed systems, and architecture, along with 10+ years of experience.

Own the roadmap for specialized machine-learning models and verticalized intelligence supporting B2B marketing and revenue workflows. Partner with product managers, data scientists, customers, and business stakeholders to translate domain-specific problems into scalable features and modeling requirements. Measure model effectiveness through business and revenue outcomes, while improving the relevance, utility, and accuracy of predictive signals and recommendations. The role requires product management experience focused on AI/ML, data products, or B2B SaaS.

Leads scalable IT architecture and integration strategies for manufacturing digital transformation. Connects MES, ERP, SCADA, IIoT, and cloud platforms; establishes architecture, data governance, and infrastructure standards; supports smart factory roadmaps; modernizes legacy systems; and partners with engineering, operations, and IT teams across manufacturing sites.

Lead the design and operation of reliable, scalable AWS infrastructure and deployment pipelines using Terraform, Kubernetes, Docker, and CI/CD tools. Establish SLOs, monitoring, observability, incident response, and error-budget practices. Integrate AI, machine learning, and AIOps for anomaly detection, diagnostics, predictive alerting, automation, and capacity planning. Mentor engineers, collaborate with development and data teams, maintain runbooks, and drive cloud-native reliability and DevSecOps improvements.

Partners with enterprise customers to understand requirements, design AI solution architectures, and guide production deployments. Builds integrations, prototypes, reusable tools, and technical assets while troubleshooting complex issues. Leads technical workshops, collaborates with Product and Engineering teams, and shares customer feedback to improve the platform. The role combines software engineering, customer engagement, AI deployment, and scalable solution development.

Design and deploy scalable AI solutions for enterprise customers. Build integrations, prototypes, reusable tools, and production-grade applications; lead technical workshops; troubleshoot complex issues; document best practices; and collaborate with Product and Engineering teams to improve the platform based on customer feedback.

Lead product analytics and data-driven decision-making across Product, Engineering, Design, and Leadership. Analyze user behavior, design and measure large-scale experiments, identify growth opportunities, influence roadmaps, and quantify business impact. The role requires advanced SQL, Python, BI, statistical modeling, and emerging AI-augmented analytics expertise, along with executive communication and team management experience in transactional or marketplace products.

Owns Sensata’s commercial AI product portfolio across sales, revenue operations, pricing, marketing, and finance. Maps workflows, identifies pain points, defines product requirements, prioritizes roadmaps, and manages backlogs. Leads pilots through production, user adoption, governance, vendor integration, and value measurement. Builds business cases, tracks revenue and productivity outcomes, and communicates product decisions to senior leaders while partnering with AI engineers, data scientists, IT, Finance, and business stakeholders.

Manages data readiness, migration, harmonization, deployment, and governance activities for Lonza’s global SAP S/4HANA transformation. The role coordinates CDMO sites, data owners, business teams, SAP integration partners, and project stakeholders across SD and PP data domains. Responsibilities include developing data preparation plans, supporting testing and cutover, rolling out SAP MDG, resolving data issues, managing documentation, delivering training, tracking risks, and driving continuous improvement and automation.

Leads research, design, development, and deployment of production-grade AI and machine learning solutions. Responsibilities include Generative AI and LLM research, model development, data pipeline engineering, data preparation, analytics platform development, software testing, CI/CD, model governance, monitoring, and lifecycle management. Collaborates with product, engineering, architecture, and business stakeholders to create scalable intelligent products and evaluate emerging technologies.

Leads research, design, development, and deployment of AI-powered solutions using Generative AI, LLMs, machine learning, and deep learning. Builds data pipelines, prepares structured and unstructured data, develops production-grade models, and establishes AI/ML governance and lifecycle practices. Collaborates cross-functionally to deliver scalable analytics products, conducts proofs of concept, and applies modern software engineering, testing, CI/CD, and MLOps practices.

Lead fleet managers and delivery operations across multiple areas and hubs. Optimize logistics efficiency, service levels, staffing, training, cost, scheduling, customer satisfaction, and delivery partner performance. Manage operational processes, reporting, retention initiatives, payout structures, compliance, dispute resolution, and strategic implementation while coaching area managers and ensuring reliable delivery execution.

Lead data science initiatives for Risk Management and Compliance, translating business problems and KPIs into actionable insights. Develop, train, test, and deploy machine learning models for credit risk, including classical, deep learning, graph network, agentic AI, and large language model solutions. Build reliable AI agents, conduct rigorous testing, collaborate with senior stakeholders, and deliver scalable data extraction, modeling, visualization, and analytics solutions.

Performs satellite image classification and time series analysis using Sentinel and PlanetScope data. Develops and optimizes machine learning pipelines, Google Earth Engine applications, geospatial models, predictive models, decision-support tools, and dashboards. Coordinates ground-truth data collection with field staff, supports stakeholder engagement, and prepares reports, research papers, and communications.

Reviews trade surveillance alerts across equities, derivatives, fixed income, and commodities to identify regulatory or policy breaches. Investigates and documents escalated alerts, supports control design and optimization, prepares management information, develops surveillance metrics, and assists with regulatory inquiries, audits, testing, and technology enhancements. Collaborates with Compliance, Operations, Technology, Risk, vendors, and front-office teams across regions.