Top Machine Learning Jobs in India

Own Capital One’s corporate communications and technology employer brand in India. Responsibilities include media relations, PR, technology content, event and sponsorship execution, agency management, executive spokesperson preparation, crisis communications, and brand governance. The role builds Capital One’s reputation as a technology leader and attracts engineering and data science talent through external and internal communications.

Designs and implements secure AI solutions and technical security controls; assesses AI systems for security and privacy risks; develops automation, monitoring, and detection capabilities; translates policies and regulations into controls; supports governance and audit readiness; and partners with engineering, security, risk, and business teams. The role also provides technical leadership and AI security education in an onsite, overnight-shift environment.

Design and maintain scalable ETL/ELT pipelines, data warehouses, and big data infrastructure. Optimize databases and workflows, implement data quality and governance frameworks, automate testing and deployment, troubleshoot complex data issues, and ensure reliable systems. Collaborate with data scientists, analysts, and business teams to support analytics and machine learning. Mentor junior engineers and establish data engineering best practices.

Lead equipment engineering for assembly manufacturing to improve safety, quality, reliability, productivity and cost. Drive equipment strategy, lifecycle management, automation, Lean/Six Sigma improvements, and AI-enabled predictive maintenance and analytics. Build team capability, deploy Industry 4.0 solutions, and partner across functions to meet OEE, downtime, and productivity targets.

Leads and develops a software engineering team building scalable, secure Data Quality Platform services. Sets technical direction for data profiling, quality rules, data contracts, anomaly detection, alerting, and incident workflows. Partners cross-functionally on roadmaps, architecture, delivery, and resource trade-offs. Establishes engineering standards and ensures reliable production operations through observability, incident management, capacity planning, and continuous improvement. Guides practical use of AI and machine learning to improve data quality workflows.

Validate NLP and Generative AI models within Model Risk Management, assessing methodology, data integrity, performance, implementation, and monitoring. Develop test models, analytical reports, validation tools, and evaluation pipelines while managing model risk across the lifecycle. Collaborate with auditors, regulators, model stakeholders, and technology partners on quantitative modeling, governance, automation, and compliance initiatives.

Performs complex analytics research and data analysis to support strategic initiatives, extract business insights, and identify engineering enhancements. Leads cross-group technical projects, mentors staff, collaborates with stakeholders, and applies risk and compliance knowledge. Desired work includes Python, AI/ML, LLM, agentic AI, unstructured data, cloud analytics, SAS modernization, visualization, and enterprise AI platform development.

Leads validation strategy and automated testing for NAND-based SSD firmware across functional, performance, reliability, interoperability, and customer qualification domains. Develops test plans and automation frameworks, debugs NVMe and SAS issues, analyzes quality risks, and drives issue closure with cross-functional engineering teams and hyperscale customers. Applies AI and machine learning to test generation, failure analysis, anomaly detection, reporting, and predictive quality. Mentors engineers and leads complex validation programs.

Designs data-driven digital products, dashboards, applications, chatbots, voice interfaces, and assistants using human-centered design, Lean UX, Design Thinking, and rapid prototyping. Leads workshops, develops personas and user journeys, creates interaction and visual designs, conducts iterative user testing, and shapes design systems across web, mobile, and IoT. Collaborates with product, technology, brand, marketing, regional, and customer teams to deliver accessible, usable, and compelling experiences.

Designs, develops, maintains, and improves scalable software applications and systems. Leads AI adoption across software deployment automation, collaborates with product and engineering teams, troubleshoots complex issues, participates in code reviews, and documents applications and deployment processes. Creates training, demonstrations, workshops, playbooks, reusable patterns, and use cases to help engineering teams and business stakeholders adopt AI solutions.

Leads validation strategy and automated testing for NAND-based SSD firmware across functional, performance, reliability, interoperability, and customer qualification domains. Develops test plans, frameworks, and continuous validation solutions; debugs NVMe and PCIe issues; analyzes quality risks; and drives cross-functional issue resolution. Partners with hyperscale customers, leads complex programs, mentors engineers, and applies AI, machine learning, and generative AI to improve test coverage, failure analysis, reporting, and validation productivity.

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.

Lead the delivery of AI products across international markets by coordinating internal engineers, contractors, and vendors. Establish engineering standards, make technical decisions, review code, ensure testing and release readiness, and guide solutions from proof of concept through enterprise production. Oversee data, AI, generative AI, agentic AI, and full-stack applications while managing dependencies, technical debt, and delivery quality. Collaborate with product, engineering, data science, security, and global stakeholders, and evaluate emerging technologies.

Develop and implement service delivery models, frameworks, and transformation initiatives to improve global efficiency and profitable growth. Monitor performance, conduct operational reviews and benchmarking, promote automation and continuous improvement, and manage AI and ML applications for service analytics. Ensure compliance with legal, financial, security, and privacy requirements while partnering with senior stakeholders on high-impact initiatives.

Owns the end-to-end architecture, security, and integration landscape for the global o9 Enterprise Operations Planning platform. Leads integrations with SAP and non-SAP systems, evaluates hosting models, governs security and performance, manages technical risks, and supports regional rollouts. Partners with business stakeholders, vendors, DevOps, and enterprise architecture teams to translate planning requirements into scalable solutions and provide go-live leadership.

Lead the design and delivery of machine learning and LLM solutions for analytics automation and business decision-making. Own the AI stack roadmap from prototyping through deployment and impact measurement, build real-time and asynchronous ML pipelines, establish standards for evaluation, observability, security, and cost management, and mentor analysts. Translate complex technical work into strategic recommendations while partnering across functions in a fast-paced B2B SaaS environment.

Develop and lead production AI/ML solutions for healthcare, including NLP, deep learning, generative AI, forecasting, and computer vision. Manage the full machine learning lifecycle from data preparation and feature engineering through deployment, monitoring, and documentation. Optimize and quantize generative AI models, build proof-of-concepts, establish development best practices, and collaborate with research, engineering, and product teams. Promote ethical AI through fairness, transparency, and accountability.

Develop and deploy advanced AI, machine learning, deep learning, NLP, and computer vision solutions for healthcare. Lead end-to-end model development, including data preparation, feature engineering, modeling, deployment, monitoring, documentation, and lifecycle management. Optimize generative AI models, apply RAG and agentic AI techniques, conduct complex proofs of concept, establish development best practices, and collaborate with research, engineering, and product teams while promoting ethical AI.

Lead fraud risk analyst who builds early-warning systems, detects transactional anomalies, analyzes merchant behavior, designs monitoring strategies, partners cross-functionally, maintains queries and visualizations, and takes action to mitigate fraud and credit risk.

Responsible for starting up, developing and optimizing assembly/process equipment to improve yield, quality, cost and productivity. Diagnose and resolve manufacturing issues using FMEA, SPC, DOE and failure analysis. Lead yield improvement, supplier/material management, process qualifications and integrate AI/data-driven methods for predictive control, anomaly detection and analytics-driven decision support.

Design and deliver end-to-end packet core and 5G core solutions, optimize network performance, collaborate with product, R&D and customer teams, lead transformations, perform protocol and data analysis, and support deployment and knowledge transfer.

Drives data and analytics initiatives for GPL and Wholesale Digital & Innovation. Responsibilities include analyzing complex business and operational challenges, sourcing and curating data, validating data integrity, developing actionable insights, supporting customer communications, and partnering with business and technology leaders. The role also identifies engineering enhancements and applies compliance and risk management requirements to analytics activities.

Lead and embed Six Sigma methodology and continuous improvement across the organization. Manage and coach teams of Six Sigma practitioners, drive process improvement projects tied to business objectives, collaborate with senior leadership and stateside partners, and apply change management and analytics (including automation and emerging ML/NLP/AI technologies) to solve major customer and operational issues.

Provides administrative, documentation, coordination, and office operations support for an engineering department. Responsibilities include managing schedules, records, reports, meetings, engineering change documentation, procurement coordination, NPI logistics, KPI tracking, equipment records, onboarding, training, and cross-functional communication. The role also supports AI- and data-driven manufacturing enablement using MES, equipment, yield, analytics, and machine learning data.

Owns a cybersecurity detection coverage area, including backlog prioritization, technical direction, detector design, implementation, code reviews, tuning, and performance measurement. The role improves detection fidelity, ATT&CK coverage, false-positive reduction, and regression prevention while translating threat intelligence, threat hunting, and SOC feedback into roadmap priorities. It requires deep expertise in at least one detection domain and hands-on detector-as-code development with version control and controlled deployment.