Top Machine Learning Jobs in India

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.

Build backend services, APIs, databases, and data pipelines for large-scale satellite imagery and geospatial analytics. Deploy and monitor self-hosted ML inference services in offline, air-gapped defense environments. Optimize batching, latency, throughput, GPU usage, observability, evaluations, and analyst-facing tooling while maintaining reproducible builds and secure deployment processes.

Build end-to-end full-stack features for air-gapped, on-premise satellite-data platforms. Responsibilities include designing APIs, schemas, queries, indexes, migrations, and data-integrity controls; creating analyst-facing interfaces with maps, imagery, dashboards, and workflows; serving tiles and dependencies locally; and debugging across the stack under limited observability. The role requires strong backend and frontend fundamentals, systematic debugging, defensive design, UX awareness, dependency discipline, and comfort with secure offline environments.

Designs, develops, optimizes, and deploys production-grade machine learning, generative AI, LLM, agentic AI, and RAG systems. Responsibilities include model and pipeline engineering, MLOps/LLMOps, cloud and enterprise integrations, evaluation, monitoring, guardrails, technical documentation, stakeholder collaboration, and mentoring engineers. The role also drives engineering standards, technical decisions, reusable frameworks, and pilots of emerging AI technologies.

Lead product strategy and roadmap for acquiring fraud and risk monitoring. Build real-time rules, risk signals, ML and AI decisioning. Reduce fraud and chargeback losses, improve recovery and operations, run experiments, monitor key metrics, and collaborate with Engineering, Data Science, Risk, Operations and Finance.

Lead the WAAP engineering product line, define AI-era API and application protection strategy, own architecture and roadmap, hire and develop engineering teams, partner with customers, ensure reliability and incident response, and collaborate across AppSec to build AI discovery, red-teaming, and AI firewall capabilities.

Design, develop, and deploy enterprise agentic AI applications using LLMs, RAG, and orchestration frameworks. Build scalable AI services, APIs, reusable components, and integrations with PLM, SAP, and other enterprise systems. Translate business needs into AI-driven workflow solutions, improve quality through prompt engineering and evaluation, and support the full software development lifecycle through production. Collaborate with business, IT, and engineering teams to deliver secure, reliable, cloud-native AI applications.

Remote, part-time freelance role to improve AI-generated Hindi content. Tasks include data collection, evaluation, annotation, pairwise comparisons, counting, and object tagging across audio, video, images. Requires native Hindi fluency and strong English; ML/data annotation experience preferred. Flexible hours, immediate start, paid hourly.

The Financial Services Leader will oversee the FS Consulting Practice, managing high-level strategy, client relationships, and operational delivery across several accounts while fostering innovation and cultural transformation.

Develop and maintain scalable data pipelines using Python and PySpark. Implement ETL processes, optimize performance, and ensure data quality while collaborating with cross-functional teams.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.