Top Machine Learning Jobs in India

Design, build, and operate observability, anomaly detection, and reconciliation tooling for monetization systems. Implement metrics/logs/traces, automated billing and data anomaly detection, SLOs, runbooks, incident response, and collaborate with product, finance, and support. Leverage telemetry and machine learning to predict and resolve monetization issues, and guide technical direction for reliability and detection work.

Design, validate, and monitor card fraud strategies using data analysis and analytics/AI. Partner with product, technology, and operations to develop, test and implement detection and treatment capabilities, balancing fraud mitigation with revenue, costs, and customer impact.

Design and build production-grade AI applications embedding ML/LLM capabilities. Integrate trained models into APIs, services, UIs, and workflows. Operationalize models (versioning, deployment, monitoring), implement guardrails, ensure security/privacy/compliance, apply bias mitigation and explainability, and mentor team members on AI engineering best practices.

Own end-to-end revenue performance across Sales and Customer Success/Growth for the InSEA region. Lead acquisition, retention, expansion, forecasting, product penetration, customer advocacy, and regional marketing partnerships. Build and scale the commercial organization, establish disciplined operating processes, and collaborate with global Analytics, Machine Learning, Product, Marketing, and Operations leaders. The role requires deep Greater China mobile advertising expertise, senior customer relationships, and fluency in Mandarin and English.

Design, develop, and deploy AI/ML and LLM solutions (NLP, RAG, embeddings, vector DB) across sales and digital use cases. Perform data analysis, build model APIs, integrate diverse data sources, identify opportunities for AI adoption, and collaborate with cross-functional teams to improve user experience and automate processes.

Design, build, and deploy ML models and end-to-end pipelines (data ingestion, feature engineering, training, validation, serving). Implement MLOps practices, containerized deployments, model monitoring, and retraining workflows. Collaborate with cross-functional teams, optimize model performance for production, write production-quality code, participate in architecture decisions, and mentor junior engineers.

Designs, builds, migrates, and operates AWS workloads and platform infrastructure. Responsibilities include cloud architecture, infrastructure as code, security, cost optimization, environment provisioning, lifecycle management, DevOps and CI/CD, container platforms, and AI foundation enablement. The role uses Terraform, CloudFormation, AWS CDK, Python, Docker, Kubernetes, and numerous AWS services while applying the AWS Well-Architected Framework.

The Senior Software Engineer will develop AI agents, design data architectures, and ensure compliance with healthcare regulations while collaborating across teams.

Leads a multidisciplinary engineering team building Testsigma’s AI agent platform, knowledge graph infrastructure, and core product. Owns engineering delivery, software quality, platform reliability, SLOs, incident response, hiring, team development, product partnership, and customer-facing technical escalations. Collaborates with product and go-to-market teams to prioritize roadmap commitments, address reliability concerns, and ensure high-quality releases.

Creates accessible data science content on machine learning and deep learning, researches state-of-the-art techniques from open-source projects and journals, and develops and supports MOOCs covering data science, computer vision, machine learning, and NLP. The role requires Python, machine learning, deep learning, NLP, strong communication skills, and a six-month availability.

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.