Top Machine Learning Jobs in India

Leads enterprise B2B sales for Analytics Vidhya’s AI training and data-culture offerings. Responsibilities include developing sales strategy, acquiring strategic clients, prospecting, managing pipelines, engaging C-level and analytics decision-makers, preparing proposals, negotiating software licenses, closing deals, and achieving revenue and territory targets across India and international markets.

Develop and maintain scalable Python backend applications using FastAPI, Django, APIs, microservices, databases, asynchronous processing, and event-driven architectures. Design efficient solutions, optimize performance, write automated tests, implement secure and maintainable code, and contribute to CI/CD deployments. Collaborate with product and engineering teams, participate in requirements gathering, analyze risks, estimate work, improve coding standards, and guide team members.

Leads the sales organization by setting strategy, managing targets, recruiting and developing sales staff, building strategic client and partner relationships, monitoring performance, forecasting revenue, managing budgets, and collaborating cross-functionally to drive market expansion and business growth.

Lead the vision, strategy, roadmap, and development of an AI Agent platform. Conduct market and customer research, translate enterprise pain points into product requirements, collaborate with AI/ML engineers, and drive go-to-market adoption and monetization. Manage cross-functional Agile execution and help scale an early-stage B2B SaaS product involving integrations, APIs, and workflow automation.

Build and operate production AI/ML systems, including LLM applications, RAG pipelines, automated model training, APIs, multimodal data pipelines, and MLOps workflows. Deploy and monitor solutions on AWS using SageMaker, Lambda, S3, Glue, EKS, and Bedrock. Implement responsible AI guardrails, data governance, security, CI/CD, model versioning, performance monitoring, and retraining processes.

Designs data science models and analytics for privileged access management in Microsoft Azure. Manages Azure PIM policies, analyzes access-risk datasets, develops dashboards, automates reporting, integrates security workflows, and troubleshoots privileged-access issues. Collaborates cross-functionally to improve Azure security, compliance, governance, and operational efficiency using Python or R, Azure data services, databases, and visualization tools.

The Chief Information Officer will lead Loom Solar’s technology strategy, digital transformation, enterprise applications, AI and automation roadmap, data and business intelligence, smart manufacturing systems, cybersecurity, and technology organization. The role focuses on integrating business systems, improving sales, dealer and customer experiences, optimizing manufacturing and supply chain operations, strengthening data-driven decision-making, and delivering measurable productivity, cost, revenue, and working-capital improvements.

Lead a team to develop and apply next-generation defect detection equipment and processes, accelerate technology transfer, mentor staff, analyze experiments and defects, build detection techniques, manage cross-functional projects, define program health metrics, and collaborate globally to improve process yield. Data analysis, ML, and statistical modeling expertise is an advantage.

Conduct STEM and emerging-technology workshops, demonstrations, presentations, and hands-on learning activities for school audiences. Research innovation trends, develop educational content and programs, demonstrate robotics, AI, ML, and maker-space concepts, and collaborate with internal teams. The role requires strong public speaking, mentoring, communication skills, a personal laptop, bilingual English and Hindi proficiency, and willingness to travel for school visits and workshops.

Leads fraud risk data analysis, statistical model development, validation, implementation, monitoring, and model risk management across digital and payment channels. Oversees governance, documentation, change management, issue remediation, regulatory engagement, and effective challenge. Partners with senior stakeholders to shape fraud strategy, communicate model findings, and improve fraud prevention while maintaining data quality, compliance, and strong governance.

Own the architecture, design, development, deployment, and support of Workday Extend applications and enterprise integrations. Lead solutions using Workday Studio, APIs, AWS, AI, and Generative AI while establishing security, integration, testing, observability, and CI/CD standards. Partner with cross-functional stakeholders, conduct technical discovery and architecture reviews, identify risks, and mentor developers. The role drives modernization of Okta’s Workday technical landscape and delivers scalable, secure, maintainable employee technology solutions.

Leads the development and operation of cybersecurity data engineering capabilities across AWS and Databricks. Builds medallion lakehouse pipelines, Python services, REST integrations, CI/CD workflows, and secure cloud-native solutions supporting detection, response, and AI-assisted security. Owns reliability, monitoring, data governance, troubleshooting, documentation, and on-call operations while mentoring engineers, coordinating global stakeholders, and leading sprint ceremonies.

Design, build, test, deploy, and monitor AI-powered solutions (Generative AI, LLMs, NLP). Develop prompts, RAG, vector search, data pipelines, APIs, and integrations. Implement responsible AI, CI/CD, and production monitoring while collaborating with stakeholders and following engineering best practices.

Design, develop, deploy, and optimize production AI/ML, generative AI, and agentic AI solutions for supply chain operations. Build forecasting, optimization, predictive analytics, RAG, and intelligent automation systems; develop data pipelines and integrate enterprise data sources. Deploy cloud-native solutions on AWS and Azure, implement MLOps and LLMOps, monitor model performance, and collaborate with stakeholders on scalable, responsible AI architectures.

Lead the Data Engineering team and own the company’s lakehouse, ingestion pipelines, orchestration, data quality, governance, reliability, cost controls, and self-service data platform. Manage batch, streaming, and change-data-capture systems serving financial data at scale. Ensure compliance with regulated-environment requirements, support machine learning data foundations, contribute hands-on code and debugging, and grow the engineering team through hiring, coaching, and performance management.

Research and develop quantitative signals from high-frequency equity market data. Responsibilities include feature engineering, exploratory data analysis, simulation and backtesting, validation across markets and regimes, signal implementation, and collaboration with quantitative researchers and engineers. The role also involves applying machine learning and deep learning to market microstructure, building production-quality low-latency code, exploring data sources, and developing tools to automate software development, testing, and deployment workflows.

Manage the enterprise governance, risk, and compliance program across HIPAA, HITRUST, SOC 2, PCI DSS, vendor risk, audits, and healthcare data protection. Lead risk assessments, control documentation, remediation, client assurance, third-party reviews, and executive reporting. Develop responsible AI governance for healthcare revenue cycle management, including privacy, security, bias, transparency, and regulatory risk controls. Coordinate cross-functional compliance initiatives and support incident response, business continuity, and employee awareness programs.

Designs, develops, tests, deploys, and supports UiPath automation solutions, including attended and unattended bots, Orchestrator workflows, and REFramework implementations. Identifies hyperautomation opportunities and builds intelligent document processing, AI-assisted workflows, RAG solutions, chatbots, AI agents, and predictive or classification models. Integrates enterprise applications through APIs, mentors developers, performs code reviews, resolves production issues, and collaborates with business stakeholders on AI use cases.

Own the strategy, roadmap, growth, and lifecycle delivery of an Exposure Management application. Translate cybersecurity, vulnerability, identity, attack-path, and control data into actionable product capabilities using AI and knowledge graphs. Partner with clients, Sales, Marketing, Engineering, and Client Solutions to prioritize features, support go-to-market activities, and drive cross-sell. Lead and develop Product Owners while advancing agile, client-focused product practices.

Drive full-cycle sales for complex, high-value AI research workspace opportunities. Originate and close new business, support strategic renewals, expand stakeholder relationships, lead value-based workshops, and translate AI and research analytics capabilities into measurable customer outcomes. Engage senior decision-makers across enterprise, government, academic, and research organizations while developing pipelines, territories, business cases, negotiations, and long-term account growth.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

Lead the design, development, and operation of enterprise Agentic AI and ML platforms. Build agent orchestration workflows, APIs, custom connectors, integrations, monitoring, observability, and reliable cloud-based systems. Establish LLMOps, MLOps, governance, security, and lifecycle-management practices; troubleshoot complex platform issues; lead technical prototypes; document architectures; and mentor engineers. The role focuses on scalable Agentic AI solutions using cloud, orchestration, and modern ML tooling.

Provide advanced analytics and AI/ML support for life sciences clients, particularly pharmaceutical commercialization initiatives. Lead India-based delivery teams, manage complex analytical projects, develop client recommendations, enhance consulting services, and support business development. The role requires hands-on work with Python, visualization tools, cloud platforms, and pharmaceutical use cases, along with mentoring, stakeholder collaboration, and communication with US clients.

Build and maintain scalable MLOps infrastructure, pipelines, GPU clusters, deployment automation, observability, and model lifecycle tooling for robotics products and research. Responsibilities include transforming prototypes into production solutions, optimizing training and evaluation pipelines, managing CI/CD and data workflows, monitoring platform health, collaborating cross-functionally, and mentoring contributors.

Conduct applied AI research in computer vision and machine learning for public safety and video security. Develop, prototype, optimize, and deploy models for edge and cloud environments, including vision-language and Transformer-based systems. Review research, create patents, collaborate with software teams, improve ML deployment processes, and establish rigorous performance evaluation frameworks. The role requires production-quality coding, dataset collaboration, and model optimization for latency, throughput, and computational efficiency.