Top Machine Learning Jobs in India

Lead end-to-end operational transformation engagements: assess operations, design process redesigns, drive RPA/AI and digital initiatives, deliver data-driven insights, manage cross-functional teams, stakeholder engagement, change management, and measure KPIs to ensure sustainable results.

Collect, clean, transform and analyze large datasets; perform feature engineering, train, evaluate and fine-tune ML and NLP models; deliver analytics-driven insights; manage client deliverables and translate business requirements into analytical solutions.

Perform marketing analytics for P&C insurance campaigns: convert business problems into analytical solutions, program in Python/SAS, run EDA, build and validate ML models, generate campaign and project performance reports, and QC results to support decision-making.

Lead risk analytics projects and a team of analysts to build, validate, monitor, and optimize fraud detection models. Perform EDA, deep learning and GNN solutions on transaction data, set monitoring KPIs, conduct root-cause analysis, collaborate with fraud strategy, and produce technical documentation.

Lead product development for payments platforms, drive integrations, ensure AML & sanctions compliance, collaborate across engineering, operations, compliance and business teams, define product vision and roadmap, perform technical solutioning, data analysis, UAT, wireframing, incident management, and support product delivery and demos.

Lead end-to-end finance automation and transformation initiatives using RPA, AI, and Six Sigma. Manage teams, engage clients, integrate AI and analytics with IT, improve AP/AR/GL processes, oversee financial reporting and compliance, and drive innovation and measurable efficiency gains.

Design and implement enterprise ML/AI and generative models (GANs, VAEs) on Azure/AWS, build RAG solutions for confidential data, conduct R&D, collaborate with clients and cross-functional teams, perform model training/evaluation on large datasets, participate in code reviews and documentation, and share knowledge. Healthcare/life-sciences experience is preferred.

Lead development of advanced analytics and machine-learning models, design algorithms from scratch, work with large datasets and relational databases, review audit findings, perform manual claims selection, generate selection queries, and potentially lead and manage a team.

Lead end-to-end finance transformation and AI initiatives—define strategy, identify automation opportunities, implement AI-driven solutions, manage stakeholders and change, establish KPIs, and drive continuous improvement across finance domains.

Design and deliver advanced data science and NLP solutions (models, algorithms, deployment, monitoring). Partner with stakeholders to translate business problems into ML approaches, ensure model quality, and deploy/monitor production models to drive customer insights and value.

Drive acquisition and growth through customer personalization analytics. Use structured and unstructured data, AI/GenAI, and experimentation to evaluate arbitration frameworks, design tests, measure performance, and recommend investment and treatment optimizations. Partner across marketing, product, technology, and analytics to implement solutions and measure business impact.

Support and execute AI-driven personalization strategies across digital channels. Analyze customer behavior, prepare data, build queries and basic models, contribute to experimentation and model evaluation, and help embed personalization features with product and engineering teams to improve engagement and conversion.

Develop, review and execute economic logic and analytical solutions to reduce credit and fraud risk. Analyze large datasets, apply big data and machine learning approaches, present findings to leadership, and collaborate with cross-functional partners to drive profitable credit decisions and improve customer experience.

Lead design, implementation, deployment, and scaling of AI-driven applications with emphasis on RAG-based chatbots. Architect solutions using Python and Java, deploy on AWS, mentor engineers, collaborate cross-functionally, ensure testing/performance tuning, and research emerging AI technologies to drive innovation.

Analyze large datasets, build and evaluate machine learning models, prepare and transform data, and create Power BI dashboards. Apply statistical methods and A/B testing, use cloud platforms (AWS, Snowflake), leverage Generative AI/LLMs, ensure data governance and responsible AI, and present insights while collaborating in an Agile team.

Design, implement, and operate scalable, secure Azure IAAS/PAAS solutions including networking, landing zones, AKS, IaC with Terraform, CI/CD via Azure DevOps, API Management, security/identity, cost optimization, troubleshooting, and mentorship.

Lead and manage client engagements end-to-end: scope, align, translate operational problems into technical briefs, track delivery, manage expectations, drive resolution, develop pipeline and grow account relationships while partnering with deployment and engineering teams.

Lead an RPA team to design, develop, test, deploy, and maintain scalable automation bots (UiPath/AA/Blue Prism). Collaborate with stakeholders to assess processes, manage projects, mentor developers, troubleshoot bots, and report progress to senior management.

Design, build, and deploy conversational AI/chatbot solutions on the Kore.AI platform. Create intents, entities, dialog flows, integrate backend systems and REST APIs, and develop responsive user interfaces with ReactJS while collaborating with UI/UX designers.

Design and implement machine learning and NLP solutions, build prototypes and demonstrations, work with LLMs and deep learning frameworks, and leverage cloud services to productionize AI models.

Design, develop, and deploy machine learning models. Prepare and preprocess large datasets, train and evaluate models, perform hyperparameter tuning and cross-validation, and collaborate with cross-functional teams to translate business requirements into AI/ML solutions. Contribute to design reviews, present findings to stakeholders, and maintain BI tools, dashboards, and model productionization under manager guidance.

Lead analytics for promotions and incentives strategy, building scalable frameworks to measure promo ROI, automate performance tracking, and apply advanced analytics/ML for segmentation, targeting, and impact measurement. Analyze seller behavior and inventory patterns, partner with cross-functional teams, and translate insights into actionable business strategies to optimize pricing and promotions at scale.

Design, develop, deploy, and operate production-grade machine learning, Generative AI, LLM, RAG, and agentic AI solutions on AWS. Build and evaluate models and pipelines, integrate enterprise systems, optimize quality, latency, reliability, security, and cost, and use Bedrock and SageMaker for production AI architectures. Monitor model performance and drift while collaborating with engineering, data, architecture, and product teams.

Lead AI/ML development initiatives: design algorithms, mentor engineers, manage development process, and deploy AI solutions to production.

Design, build, deploy, and maintain production AI/ML applications across the full ML lifecycle. Develop and productionize models, LLM-based and generative AI solutions, create scalable APIs and MLOps pipelines, optimize performance and cost, and collaborate with product and data teams. Mentor engineers and promote engineering best practices.