Top Machine Learning Jobs in India

Manage and evaluate scientific manuscript submissions in computer science, electrical engineering, data science, machine learning, and related fields. Coordinate peer review, provide author support, recruit research papers through scientific outreach, attend conferences and academic visits, and help strengthen journal visibility. The editor will also contribute to journal strategy, science communication initiatives, and collaborative editorial planning.

Leads the architecture and development of enterprise AI, machine learning, and generative AI solutions. Builds data pipelines and platforms, develops and deploys ML models, creates LLM applications using RAG and agents, and integrates AI with enterprise systems. Establishes AI security, evaluation, observability, and governance practices; leads proofs of concept through production; and mentors engineering teams.

Lead a software engineering team developing Windows device optimizations across hardware, firmware, operating system, services, and applications. Set technical direction and roadmap, guide complex performance, reliability, debugging, power, and telemetry initiatives, and deliver reliable solutions at scale. Hire, coach, and retain engineers while collaborating with Windows teams, OEMs, silicon vendors, hardware partners, and senior stakeholders.

Evaluates and manages machine learning research manuscripts for Nature Communications, overseeing peer review, editorial decisions, author and reviewer relationships, and publishing workflows. The editor will help shape journal strategy, attract high-quality submissions, commission reviews, represent the journal at conferences, monitor scientific trends, and contribute to scientific content and editorial policy development.

Leads area managers and delivery fleet operations across multiple hubs. Responsibilities include optimizing order fulfillment, logistics, service levels, staffing, training, payouts, customer satisfaction, compliance, process improvements, cost analysis, reporting, and dispute resolution. The role manages large teams, supports strategic initiatives, conducts hub visits, and ensures delivery executives meet performance, scheduling, safety, and brand standards.

Design and implement scalable software architectures for semiconductor equipment solutions. Develop prototypes, lead complex software components, conduct technical reviews, mentor engineers, evaluate technologies, mitigate technical risks, and contribute to high-level and low-level design. The role requires expertise in Java, Spring Boot, microservices, workflow frameworks, reactive programming, algorithms, and large-scale systems.

Leads complex technical programs for Plume’s cloud-based smart-space platform. Owns project scope, schedules, risks, dependencies, roadmaps, stakeholder communications, milestone reviews, executive decisions, and recovery strategies. Partners closely with product, firmware, driver, hardware, QA, and engineering teams on Wi-Fi feature development, interoperability, performance optimization, certification, release planning, defect triage, and successful end-to-end delivery.

Leads a large financial crime operations portfolio supporting Australian banking operations from India. Responsibilities include transaction monitoring, KYC/CDD, screening, fraud investigations, remediation, governance, risk controls, regulatory compliance, transformation, technology adoption, operational performance, stakeholder engagement, and leadership development. The role oversees complex teams, drives sustainable service and quality outcomes, manages incidents and control gaps, and builds future-ready capabilities through automation, analytics, and continuous improvement.

Supports product management and data-driven decision-making for pharmaceutical R&D technology. Responsibilities include stakeholder coordination, KPI definition, product backlog prioritization, requirements and user-story development, AI solution analysis, regulatory compliance, process mapping, UAT, training, change management, reporting, and developer guidance. The role requires business analysis experience with AI and machine learning automation, real-world healthcare data, generative AI, Agile delivery, pharma regulations, and complex data interpretation.

Leads consumer media measurement, marketing mix modeling, attribution validation, advanced analytics, and brand analytics for U.S. pharmaceutical brands. Manages and develops an analytics team, establishes scalable workflows and quality standards, supports knowledge transfer to the Hyderabad capability center, and partners with global commercial, marketing, media, agency, and analytics stakeholders. The role also drives data quality, dashboarding, reporting improvements, and use of AI and machine learning to improve commercial insights.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Seeking senior software engineers with proven expertise in at least one technical area, including AI/ML, LLMs, data, full-stack or backend development, automation, cloud infrastructure, and DevOps. Responsibilities include solving complex problems independently, collaborating across technologies, and delivering production-quality projects in a startup environment. Engagements may be project-based or ongoing.

Lead and grow a backend engineering team building scalable, cloud-agnostic infrastructure and services for big data and ML workloads. Hire engineers, enforce technical standards, partner with product and customers, define roadmap, and coordinate cross-team execution to deliver reliable, monitored, and tested systems at scale.

The VP of Data Science will define and execute the company’s AI strategy, transform cybersecurity products into AI-first platforms, and lead development of production-grade machine learning capabilities. Responsibilities include building data science teams, establishing MLOps and AI architecture standards, deploying autonomous and LLM-driven workflows, governing responsible AI use, partnering across Product and Engineering, and representing the company through client engagements, research, publications, and industry thought leadership.

Leads GxP validation, qualification, and testing across applications, data platforms, cloud infrastructure, and enterprise systems. Develops risk-based assurance strategies using CSA principles, authors and approves validation deliverables, oversees contractors and partners, supports AI-enabled application assurance, and improves validation methodologies, tools, automation, and audit readiness in collaboration with Digital, Quality, Compliance, and system owners.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

Designs, develops, deploys, and supports end-to-end AI applications for smart manufacturing. Responsibilities include building data pipelines, predictive models, APIs, web applications, LLM and agentic AI integrations, robotics and MES integrations, containerized services, and automated ML systems. The role also contributes to CI/CD, monitoring, model evaluation, Responsible AI, optimization, testing, documentation, and production support across global manufacturing operations.

Collect, clean, preprocess, and analyze structured and unstructured data. Perform exploratory data analysis, build and evaluate basic machine learning models, write SQL queries, create dashboards and visualizations, apply statistical techniques, and document findings while supporting data science projects.

Leads procurement data governance, quality operations, master data management, performance metrics, reporting, and AI-readiness strategy. Owns governance frameworks, data quality KPIs, lineage, dictionaries, glossaries, cross-system mapping, and agent-enabled operating models. Manages a multi-region team and partners with IT, security, compliance, AI, and procurement stakeholders to improve data reliability, ERP readiness, automation, and measurable business value.

Leads advanced data science and machine learning initiatives for global-scale digital map-making. Defines technical direction, develops predictive and statistical models, applies AI technologies, and drives deployment across scalable big data environments. Provides technical leadership for complex projects, collaborates with engineering and architecture teams, explains predictive solutions to executives, and mentors data scientists and engineers.

Develops machine learning and deep learning models for biologicals discovery in crop health. The role integrates genomic, metabolomic, and phenotypic data to prioritize microbial and natural product candidates, applies modern AI methods, and partners with scientists across biology, chemistry, genomics, and data engineering. Responsibilities include framing research problems, defining evaluation strategies, interpreting model outputs, generating biological hypotheses, and advancing predictive solutions through integrated discovery pipelines.

Manages operational relationships with member banks, resolving escalations and improving payment-processing performance. Oversees settlement, reconciliation, chargebacks, disputes, compliance follow-ups, and KPI governance across digital payment systems. Performs advanced analysis using Excel, Power BI, and SQL; prepares dashboards and leadership reports. Leads training, knowledge-sharing, process optimization, automation, and AI/ML initiatives while coordinating stakeholders and supporting regulatory responses.

Develops and deploys generative AI solutions, machine learning pipelines, and AI-powered applications for global market access and healthcare economics. Responsibilities include building and operationalizing models, developing RAG and LLM solutions, applying MLOps practices, partnering with business and IT stakeholders, communicating AI insights, and guiding adoption of emerging technologies. The role requires expertise in Python, deep learning frameworks, cloud platforms, vector databases, prompt engineering, model fine-tuning, and unstructured data.

Build and extend evaluation harnesses, benchmark and regression tracking, data-quality tools, training monitoring, and internal research tooling for healthcare foundation models. The role includes rotating across data, evaluations, and training infrastructure with mentorship, requiring strong Python coding, defensible machine-learning fundamentals, and evidence of independently building useful projects.

Build and extend LLM evaluation harnesses, benchmark sweeps, dashboards, regression tracking, data-quality tooling, and training-run monitoring. Develop internal tools that improve research productivity while gaining experience in pretraining and post-training workflows. The role emphasizes strong Python coding, debugging, machine learning fundamentals, experimentation, and rapid learning, with opportunities to contribute to open-source medical models serving Indian-language healthcare use cases.