Top Machine Learning Jobs in India

Leads technical programs for digital payment transformation and transaction authorization optimization. Responsibilities include product backlog management, requirements prioritization, project planning, product governance, operational support for AI-driven recommendations, and cross-functional delivery with engineering, data science, operations, and design teams. The role also develops product roadmaps, business cases, success metrics, and scalable integration solutions while supporting data security, compliance, architecture reviews, and stakeholder alignment.

Develops analytics and AI-driven tools for pre-sales value quantification. Manages large datasets using SQL, performs statistical analysis, and creates dashboards in Tableau or Power BI. Builds ROI calculators and web-based solutions, generates actionable insights, and collaborates with Sales, Marketing, Consulting, and Product teams to support customer engagement and strategic decisions.

Leads workforce automation, AI, and strategic technical programs that improve operational efficiency and support enterprise transformation. Partners with engineering, product, and business teams; manages Agile delivery, cross-functional dependencies, integrations, schedules, risks, governance, and executive communications. Also supports workforce planning, talent mobility, organizational transformation, and continuous process improvement across globally distributed teams.

Leads the architecture and implementation of scalable data platforms, pipelines, ETL/ELT workflows, and cloud-native AWS data systems. Builds and optimizes Python, SQL, and Airflow pipelines; manages data warehouses, modeling, governance, quality, lineage, and security. Provides technical leadership and mentorship while collaborating with analytics, product, engineering, and business teams. Develops reliable, cost-efficient, highly available data infrastructure and translates business needs into scalable technical solutions.

Lead the design, development, and maintenance of scalable .NET microservices across Azure and AWS. Establish CI/CD, automated testing, infrastructure as code, and cloud optimization practices. Diagnose application issues, improve scalability and availability, and provide technical leadership through architecture, code reviews, design documentation, mentoring, and proof-of-concept development. Build solutions using Kubernetes, Cosmos DB, Azure services, modern front-end frameworks, AI tools, and RESTful APIs.

Develop and deploy machine learning models for healthcare prediction, classification, intent understanding, and patient navigation. Own the full lifecycle from feature engineering and experimentation through production deployment, monitoring, and governance. Apply classical ML, deep learning, transformers, generative AI, and LLM techniques. Collaborate with product, engineering, research, and business stakeholders while ensuring responsible AI, fairness, transparency, and compliance.

Lead product strategy and roadmap development for ServiceNow’s AI Agent Platform. Prioritize features, create technical AI proofs of concept, collaborate with engineering, design, and AI research teams, and demonstrate solutions to customers and internal stakeholders. Translate customer requirements into functional solutions, define KPIs, track outcomes, and communicate recommendations on emerging AI and platform technologies.

Leads the end-to-end development, productization, deployment, and lifecycle management of AI, ML, GenAI, and agentic AI solutions for autonomous network operations. Owns AI product roadmaps, architectures, MLOps, adoption metrics, customer value, and scalable deployment frameworks. Partners with customers, product and engineering teams, mentors AI professionals, and drives innovation across telecommunications networks, including 5G, 6G, RAN, Core, Transport, and OSS/BSS.

Develop predictive models, machine learning solutions, business intelligence capabilities, and analytical programs using R and Python. Analyze large datasets, conduct scorecard and portfolio analytics, generate recommendations, support client and product initiatives, and present findings to stakeholders. The role also involves data extraction, segmentation, documentation, performance monitoring, and collaboration with project managers and analytics teams.

Execute T+1 control and AFR reconciliations, investigate and resolve discrepancies, coordinate with technology and business stakeholders, maintain reconciliation data and logic, escalate unresolved issues, and produce recurring reports. The role supports trade processing from order entry through settlement while improving operational efficiency, data quality, and process resilience.

Analyze complex datasets using advanced analytics, data mining, SQL, and visualization tools to generate actionable business insights. Partner with stakeholders and senior leaders to guide strategy, improve product performance, identify opportunities, and solve quantitative business problems. Develop data models and analytics strategies, assess data quality, identify anomalies, communicate findings clearly, and independently manage analytical programs and process improvements.

The Staff Data Analyst will transform data into insights for GitLab’s Support Engineering organization. Responsibilities include capacity and budget modeling, utilization scorecards, support economics analysis, performance indexes, AI adoption ROI dashboards, predictive churn analytics, customer-schema standardization, executive reporting, and ad hoc analysis. The role partners with Support Engineering, go-to-market, Business Operations, and Product Engineering in a remote environment.

Principal Engineer responsible for enterprise HR data engineering strategy, architecture, modernization, and delivery. Leads migration to cloud-native lakehouse platforms, designs scalable data pipelines and frameworks, and provides hands-on technical leadership across Azure and GCP. Drives AI and Generative AI adoption, governance, observability, data quality, and responsible implementation. Advises senior leadership, mentors engineering teams, and aligns secure, resilient technology solutions with business, risk, and regulatory objectives.

Manage verification for complex DRAM design projects, develop methodologies, lead teams, provide technical advice, and enhance AI-driven verification processes.

Leads strategic business analysis and product delivery for Capital One’s AML organization. Develops business strategies, requirements, analytical solutions, and data-driven products addressing money laundering, terrorist financing, and related risks. Partners with technology, model risk, and business teams; manages multiple initiatives; communicates complex analytics to senior stakeholders; and may mentor analysts. The role requires advanced quantitative analysis, statistical and machine learning knowledge, and strong executive communication.

Design and deploy AI/ML capabilities for Ericsson products, including intelligent automation, operational analytics, generative AI, knowledge management, and developer-assistance solutions. Lead innovation from research through production, influence AI/ML architecture and product strategy, and collaborate with product and engineering teams to deliver scalable, cloud-native systems.

Develop and productionize secure, reliable AI/ML solutions for complex business problems. Responsibilities include stakeholder engagement, data wrangling, analysis, modeling, NLP, end-to-end solution delivery, model documentation, research, and coaching team members. The role requires strong machine learning, Python, distributed systems, Spark, Hive, and SQL experience, with opportunities for technical leadership and cross-firm collaboration in financial services.

Leads the design, development, deployment, and monitoring of production machine learning solutions, including generative AI. Partners with product, engineering, data, risk, and governance teams to identify use cases, establish scalable architecture and evaluation practices, automate operations, assess vendors, and mentor engineers. The role emphasizes secure, resilient, responsible, and supportable AI systems in a regulated financial-services environment.

Leads development of reusable software components, APIs, automation, infrastructure, and cross-product engineering capabilities. Designs secure production solutions using Java, Python, React, cloud-native technologies, CI/CD, and infrastructure as code. Establishes governed AI-assisted engineering workflows, validates AI outputs, improves observability and application resiliency, standardizes engineering practices, mentors developers, and partners with product and architecture teams to deliver strategic initiatives.

Design and build scalable SDET testing infrastructure for data connectors, parsers, and ingestion platforms. Develop reusable testing frameworks, CI/CD integrations, AI-powered test generation, monitoring and reporting systems, utilities, and testing standards. Lead technical architecture decisions while ensuring quality, reliability, and accuracy across distributed systems. The role requires programming expertise in Python or Go, cloud platform experience, containerization, orchestration, and scalable automation framework development.

Supports clinical development programs through statistical analysis, clinical data science, machine learning, visualization, and interpretation of clinical, biomarker, real-world, and other data. Partners with cross-functional study teams to create analysis-ready datasets, reproducible workflows, decision-support insights, and submission-ready deliverables. Reviews analytical outputs, identifies data and methodological risks, supports regulatory-quality standards, mentors junior staff, and advances automation and AI-enabled analytics.

Owns the strategy, roadmap, delivery, and adoption of enterprise AI enablement platforms and AI products. Responsibilities include defining AI capabilities, prioritizing use cases, managing product lifecycles, partnering with engineering and data teams, establishing responsible AI governance, managing budgets and vendors, and influencing senior stakeholders. The role also mentors product teams and drives AI adoption, evaluation, monitoring, compliance, and organizational transformation.

Leads clinical data science and statistical analysis across pharmaceutical development studies. Designs analyses, predictive models, simulations, and integrated data reviews using clinical, biomarker, real-world, and external data. Oversees analysis plans, data quality, vendors, regulatory submissions, and reproducible workflows while applying AI/ML and CDISC standards. Partners cross-functionally, mitigates delivery and regulatory risks, mentors colleagues, and advances data science capabilities through automation, standards, publications, and external collaboration.

Analyzes, audits, designs, and optimizes telecom networks across RAN, transport, core, operations and maintenance, and services domains. Supports pre-sales and service delivery by gathering customer requirements, defining scopes of work, estimating costs, developing service proposals, and presenting value propositions. The role also addresses technical challenges, recommends solutions, applies automation and machine learning tools, and manages customer and stakeholder collaboration.

Owns enterprise AI platform architecture at global telecom scale, spanning cloud-native ML/LLM platforms, MLOps, RAG, vector databases, knowledge graphs, and agentic AI. Defines reference architectures, governs production delivery, leads architecture reviews, and establishes standards for model operations, security, observability, compliance, reliability, and cost. Designs multi-cloud inference, self-hosted and commercial model integrations, agent orchestration, and secure integrations with operational systems. Requires extensive software engineering and architecture experience, advanced AI/ML expertise, and strong cloud, security, and enterprise architecture skills.