Top Machine Learning Jobs in India

Lead operational assessments and transformation engagements: redesign processes, drive continuous improvement (Lean/Six Sigma/Kaizen), implement digital automation (RPA, GenAI, ML), leverage advanced analytics and data visualization, manage stakeholders and change, deliver project governance, and measure impact via KPIs.

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, implement, and optimize machine learning models for credit risk (PD, EAD, LGD, underwriting, collections). Analyze large datasets, refine models, apply advanced ML techniques, produce actionable insights, and communicate methodologies and results to technical and non-technical stakeholders.

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.

Lead design, implementation, and fine-tuning of generative models and LLMs for production chatbots. Apply NLP techniques, preprocess large datasets, develop and deploy models using Python and ML frameworks, collaborate with cross-functional teams, and improve conversational performance and user experience.

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.

Lead the AI Capability Centre, driving strategic development and operational excellence in AI solutions while managing a team of AI experts.

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, develop, and productionize AI/ML solutions on Snowflake (Snowpark, Cortex AI, AI SQL). Optimize Snowflake performance and cost, build and deploy ML models, integrate GenAI/LLM solutions (NLP/chatbots/semantic search), and collaborate with architects and data engineering to deliver secure, reliable software solutions.

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.

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 end-to-end people analytics: design, build, and deploy AI/GenAI-augmented analytics products and executive dashboards. Partner with HRIS, IT, and business leaders to ensure data integrity and governance across systems (e.g., Workday, Greenhouse). Develop AI/ML solutions for workforce planning and attrition prediction, translate insights into strategic recommendations for senior leadership, set analytical standards, and upskill stakeholders in responsible AI use.

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.