Top Machine Learning Jobs in India

Analyze large datasets, investigate data quality and lineage issues, document data sources and methodologies, build data pipelines, develop logical data models, and apply advanced analytics and AI/ML to identify trends. Create dashboards and automated reports, communicate complex findings, recommend operational improvements, and influence stakeholders. The role also involves managing risk and controls, supporting governance, and potentially leading or coaching analytical teams.

Deliver global people analytics projects end-to-end: gather requirements, extract and model HR data, build predictive ML/NLP solutions, define People KPIs, create visualizations, and present insights to HR and business leaders to improve workforce decision-making.

Develop and maintain scalable cloud-native SaaS software using Go and modern technologies. Responsibilities include hands-on coding, automated testing, cloud infrastructure, infrastructure as code, monitoring, troubleshooting, code reviews, technical design, mentoring, and cross-functional collaboration. The role requires experience with REST APIs, databases, containers, microservices, CI/CD, security practices, and Agile delivery, with opportunities to contribute to migrations and performance improvements.

Develop and maintain scalable cloud-native SaaS software using Go and modern technologies. Design architectures, build REST APIs and microservices, create automated API and UI tests, manage cloud infrastructure, and support CI/CD. Perform code reviews, troubleshooting, monitoring, root cause analysis, and security improvements while collaborating across global teams and mentoring junior engineers.

Design, build, and deploy production-ready AI/ML systems using cloud and on-prem pipelines. Apply generative AI, deep learning, chatbots, and image processing. Lead and mentor the team, drive cross-team decisions, and evaluate emerging AI technologies to enhance scalable solutions.

Leads the design, development, and maintenance of scalable search and backend systems. Deploys and manages machine learning models with data science teams, optimizes search algorithms and ML pipelines, integrates APIs and services, and contributes to architecture, code reviews, and engineering best practices. The role also includes technical leadership, mentoring, and collaboration across stakeholders.

Lead the design, development, deployment, and maintenance of scalable cloud-native platform capabilities for Walmart Connect’s sponsored search ad server. Build infrastructure, tooling, AI/ML frameworks, and distributed systems; conduct design reviews; drive software lifecycle activities; mentor engineers; influence architecture and technical decisions; and collaborate across teams to translate complex business needs into reliable, data-driven solutions.

Develop and deploy production-ready AI and machine learning applications using cloud AI services, generative AI, deep learning, neural networks, chatbots, and image processing. Responsibilities include translating stakeholder requirements into technical solutions, researching emerging AI technologies, preprocessing large datasets, testing and validating models, collaborating with teams, and mentoring junior colleagues.

Lead and execute Codingal's curriculum roadmap across Coding, Math, and ELA. Manage a curriculum team and external SMEs, ensure lesson and assessment quality, strengthen teacher training and evaluation, integrate AI into learning experiences, and collaborate with product, engineering, and business teams to improve student engagement and retention. Build scalable curriculum systems, feedback loops, and launch processes to drive measurable learning outcomes and operational consistency.

The role involves leading analytics initiatives for CPG clients, managing client workshops, and ensuring high-quality project delivery through cross-functional team collaboration.

Lead design and development of a shared, reusable engineering platform and codebase for large-scale retrieval systems. Integrate new retrieval methods, define APIs and evaluation frameworks, improve developer workflows, and drive cross-team adoption and technical quality standards.

Evaluate AI outputs across search, tool use, and end-to-end workflows; label failure modes; maintain benchmark datasets, golden sets, and regression slices; analyze hallucinations, retrieval issues, grounding, and workflow completion; calibrate human graders and validate LLM-as-a-judge results; improve rubrics and labeling quality; and partner with Engineering, Product, QA, and evaluation teams on quality reporting and release readiness.

Support customer onboarding and deployments by configuring platforms, performing basic integrations, handling first-response incidents, documenting runbooks and templates, surfacing recurring issues, and collaborating with Product Support and Engineering under senior guidance.

Support customer onboarding, configuration, basic integrations and environment setup. Execute defined technical tasks, own first-response incidents for assigned accounts, create reusable templates and documentation, surface recurring issues, and participate in knowledge-sharing and peer reviews under guidance from senior engineers.

Lead the design and delivery of AI platforms, manage technical teams, ensure security, and mentor talent in a cloud environment.

Lead hands-on experimentation and prototyping of Generative and Agentic AI solutions (LLMs, RAG, vector retrieval). Build production-ready POCs, reusable prompts, architectures, and implementation assets; enable engineering adoption; apply AI development lifecycle practices, evaluation, cost optimization, and governance; measure POC-to-production adoption and business impact.

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.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

Design, configure, and deploy o9 Demand Planning solutions; develop forecasting models and platform plug-ins; support demand sensing, forecast enrichment, consensus planning, and forecast accuracy initiatives. The consultant will analyze performance, gather business requirements, participate in testing and deployment, and provide post-production support while collaborating with demand planning, supply planning, and S&OP teams.

Lead and execute complex control testing for ITGCs, application and cloud security controls, automated and manual controls. Identify control design and effectiveness gaps, coordinate with stakeholders and auditors, support audit readiness, drive remediation and process transformation, mentor juniors, and contribute to testing methodology and tooling enhancements.

Provide teaching support for an undergraduate Introduction to Artificial Intelligence course: contact with students, grading, proctoring exams, and other assigned duties. Must have strong ML knowledge and hands-on proficiency with PyTorch and GPU programming. Selection includes interview; transcripts may be requested for preferred candidates.

Lead analytics strategy across data collection, BI, ML and automated analytics to enable data-driven decisions. Set standards, solve complex business problems, influence senior stakeholders, drive product/process improvements, and guide organizational units to deliver measurable business value.

Lead data science initiatives for Quvia’s connectivity platform by developing predictive, prescriptive, forecasting, classification, and optimization models. Integrate anomaly detection, knowledge graphs, neural networks, and generative AI into products; deploy and continuously refine production-ready models; establish data quality and governance standards; document workflows; and mentor junior data scientists while collaborating cross-functionally with engineering and product teams.

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.

Analyze large-scale transactional, operational, and customer datasets; build visualizations, machine learning models, and experiments; translate complex findings into clear recommendations for operational teams and executives; and apply analytical software, databases, visualization tools, and programming languages in a fast-paced blockchain environment.