Top Machine Learning Jobs in India

Lead customer engagements to develop and deploy digital implementation methodologies from RTL-to-GDSII. Troubleshoot complex tool and flow issues, evangelize new technologies, mentor AEs, collaborate with product and sales teams, and implement ML-based solutions to improve productivity and PPA.

The Director of Data Science will lead the development of AI systems, manage a team, and drive knowledge sharing while ensuring project alignment with business goals.

Sell and position the FICO Platform to enterprise clients in consumer lending and retail banking. Build senior stakeholder relationships, uncover decision-management opportunities, collaborate with pre-sales and internal teams to design solutions, and drive long-term, profitable customer engagements. Manage quarterly and annual sales targets and travel up to 50% to engage customers across Southeast Asia and adjacent industries (fintech, telecom, media, entertainment).

The Senior Data Analyst will leverage data to inform strategic decisions, analyze complex datasets, develop predictive models, and communicate insights to stakeholders.

The Forward Deployed Engineer is responsible for designing, developing, and deploying agent-based AI solutions using the Workato platform, collaborating with cross-functional teams and ensuring efficient AI workflows for enhanced IT operations.

Lead AI/ML initiatives as an individual contributor: design and implement ML solutions, develop and maintain code, define architecture, guide data infrastructure, and collaborate with cross-functional teams to solve business problems.

Analyze supply chain KPIs, troubleshoot complex planning issues, develop process and system improvements, automate analytics and data governance, support users with system questions, and drive cross-functional continuous improvement.

Owns the roadmap, delivery, and adoption of AI-enabled Integrated Business Planning solutions for global wind manufacturing. Partners with Supply Chain, Operations, Finance, and Digital teams to drive automation, predictive insights, demand and supply planning improvements, and governance. Leads design, integration, and Agile delivery of scalable data and AI products while driving user adoption and continuous improvement.

Architect and scale production ML and LLM systems for Cohesity’s Gaia Emblem engine, including RAG pipelines, model serving, GPU-enabled Kubernetes infrastructure, distributed microservices, APIs, and data pipelines. The role establishes technical direction, engineering standards, observability, CI/CD, and infrastructure-as-code practices while mentoring engineers and partnering with Product, Applied ML, Data Engineering, and SRE teams. It also involves hands-on coding, system debugging, performance optimization, and evaluating emerging ML infrastructure technologies.

Researches, designs, develops, optimizes, and deploys machine learning and generative AI solutions. Responsibilities include data preprocessing, feature engineering, model evaluation, production monitoring, system integration, design reviews, technical leadership, mentoring, stakeholder presentations, and establishing engineering standards. The role requires expertise in advanced deep learning, agentic frameworks, lineage tracking, AI/ML evaluation, statistical modeling, and production software engineering.

Build, deploy, and operate production-grade ML systems and retrieval-driven LLM agents. Design and evaluate LLM-based agents, scale ML applications, reduce technical debt, collaborate across teams, and drive the full ML lifecycle from data to production monitoring.

Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Design, develop and deliver enterprise-grade Pega solutions. Collaborate across teams, ensure code quality, integrations (REST/SOAP/Kafka/MQ), performance tuning, security, DevOps pipeline ownership, production stability and lead distributed Agile teams.

Lead end-to-end analytics and AI for Growth & CLM: build dashboards, tracking, AI/agentic workflows, and automation using Python, SQL and LLMs; partner with Product, Marketing, Risk and Ops to identify growth opportunities, run experiments, and drive measurable business optimizations.

Lead technical direction and delivery for a core NIRA backend domain: design and ship high-volume web APIs, own architecture for distributed and queue-based systems, drive complex initiatives, mentor engineers, lead incident response, champion AI dev tools, and align cross-functionally to simplify systems and influence roadmap.

Work with cross-functional teams to define and deliver analytics-driven business strategies and product requirements. Lead projects that improve pricing, shipping, conversion, supplier intelligence, and automation. Build and evaluate statistical/ML models, translate analytics into product outcomes, and communicate findings to executives.

Lead data and analytics strategy for Global Stores, defining architecture and roadmaps, delivering scalable data platforms and AI solutions (Gen AI/Agentic AI), driving cross-functional delivery, ensuring data quality/security, and mentoring engineering teams.

Lead design and delivery of scalable automation and AI/BI solutions for KYC operations. Build advanced Tableau dashboards, integrate ML/AI workflows, validate outputs, ensure governance and compliance, mentor technical teams, and collaborate with stakeholders to translate business requirements into production-ready solutions.

Lead design and delivery of scalable BI and AI-driven automation for KYC operations. Build advanced Tableau dashboards, integrate ML models, ensure output governance, collaborate with engineering and business stakeholders, and mentor technical teams.

Lead data science initiatives using advanced statistical techniques and machine learning to produce actionable insights. Partner with cross-functional teams to design data-driven solutions, manage and ensure integrity of large datasets, develop predictive models to forecast trends, and communicate results to stakeholders while staying current on emerging technologies.

Design and lead enterprise-scale AI, machine learning, and Generative AI solutions from architecture through deployment. Build data pipelines, feature engineering frameworks, RAG applications, AI agents, and LLM-powered systems. Establish MLOps practices for versioning, experimentation, monitoring, and CI/CD. Partner with stakeholders on AI initiatives, ensure security and governance compliance, and mentor ML engineers, data scientists, and AI teams.

Design and deploy production AI/ML systems, including LLM applications, RAG pipelines, automated model training, APIs, and multimodal data pipelines. Operationalize models with MLOps practices such as CI/CD, monitoring, versioning, and retraining. Build solutions on AWS using SageMaker, Lambda, S3, Glue, EKS, and Bedrock. Implement responsible AI guardrails, data governance, security, and compliance for sensitive enterprise data.

Develop, deploy, and maintain scalable AI/ML pipelines and production systems, including LLMs and generative AI models. Collaborate with data scientists, software engineers, architects, and clients to integrate AI capabilities into products. Optimize model performance, latency, and cost on AWS, GCP, and Azure, using Docker, Kubernetes, CI/CD, monitoring, versioning, and other MLOps practices.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.