Top Machine Learning Jobs in India

Supports settlement risk management across payment clearing and settlement activities. Responsibilities include monitoring credit, liquidity, operational, concentration, and participant default risks; assessing exposures, limits, collateral, and breaches; developing risk policies, dashboards, reports, and governance materials; supporting stress testing and control assessments; tracking remediation actions; and providing independent oversight and challenge to risk owners. The role also analyzes risk data, supports GRC tool implementation, monitors regulatory developments, and contributes to AI-enabled risk monitoring.

Leads the design and development of scalable AI solutions, serving as a technical subject matter expert for complex projects. Responsibilities include collaborating with product and business teams, managing the end-to-end AI development lifecycle, mentoring AI engineers, allocating resources, and ensuring technical excellence. The role also involves applying machine learning methodologies, integrating AI into existing systems, and driving cloud development initiatives.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Lead complex global supply chain planning implementations and consulting engagements. Architect scalable solutions using BY ESP and comparable platforms, guide customer solutioning discussions, manage executive stakeholders, and deliver measurable transformation outcomes. Apply supply chain domain expertise, data models, cloud architectures, integrations, and AI-enabled planning approaches to improve deployment speed and customer adoption.

Build and own scalable AI and Generative AI platforms using Python, microservices, cloud infrastructure, and machine learning models. Design backend architecture, deploy and optimize ML systems, manage Kubernetes and Terraform-based infrastructure, maintain databases and observability platforms, and lead production reliability practices. The role also involves technical leadership, mentoring, architecture decisions, incident response, and translating client requirements into robust technical solutions.

Leads advanced data science projects using machine learning, statistical modeling, and data analysis to deliver business insights. Serves as a technical subject matter expert, translates business needs into analytical solutions, mentors junior data scientists, manages end-to-end projects, and contributes to the team’s strategic direction and methodological improvement.

Leads advanced data science projects using machine learning, statistical modeling, and data analysis. Translates business needs into analytical solutions, manages end-to-end projects, mentors junior data scientists, develops algorithms, addresses complex data challenges, and contributes to the strategic direction of the Data Science team.

Leads the strategy, design, development, and deployment of Generative AI and LLM solutions for software and platform clients. Responsibilities include identifying AI opportunities, developing roadmaps, managing cross-functional projects, overseeing model development and integration, managing client relationships, identifying expansion opportunities, and mentoring AI professionals. The role also manages project scope, resources, risks, timelines, budgets, and quality standards.

Develop and optimize computer vision, machine learning, deep learning, and image processing algorithms for semiconductor inspection and metrology systems. Responsibilities include defect detection, segmentation, registration, classification, benchmarking, validation, performance optimization, and large-scale data analysis. The role collaborates with product, software, HPC, applications, and systems teams to integrate algorithms into production software and resolve customer issues.

Leads a technical team developing computer vision, machine learning, image processing, and deep learning algorithms for semiconductor inspection and metrology systems. Defines architecture and technical roadmaps, drives high-performance CPU/GPU implementations, oversees validation and product releases, resolves customer issues, and partners with global software, HPC, systems, product, and applications teams. Responsible for hiring, mentoring, succession planning, and cross-functional delivery.

Full-time summer intern supporting AI-enabled biometrics and clinical data solutions. Responsibilities include developing and evaluating machine learning, LLM, and agentic workflows; building scalable pipelines integrating clinical and statistical datasets; creating visualizations and analytical applications; automating statistical programming and clinical data lifecycle processes; and contributing to validation, documentation, traceability, and compliant deployment. The intern will collaborate with statisticians, programmers, data scientists, and clinical development teams.

Build and deploy production AI systems, including LLM agent systems, RAG applications, data pipelines, forecasting models, and decision-support tools. Responsibilities include integrating structured data and APIs, deploying scalable ML infrastructure with Docker, Kubernetes, FastAPI, and cloud platforms, monitoring model performance, and translating business problems into AI solutions. The role requires strong Python, machine learning, LLM, data engineering, and production software development skills.

Leads the design, development, deployment, and optimization of machine learning and deep learning solutions. Defines AI strategies and roadmaps, develops production-ready pipelines, implements MLOps practices, and explores Generative AI, LLM, and RAG applications. Collaborates with technical and business stakeholders, mentors AI/ML engineers, monitors model performance, and ensures privacy, security, responsible AI, and compliance.

Leads advanced identity threat research and threat hunting across hybrid and multi-cloud environments. Develops behavioral models, detection algorithms, correlation rules, and identity-focused threat intelligence for an ITDR platform. Partners with Product and Engineering, maps research to MITRE frameworks, investigates vulnerabilities, publishes technical content, pursues patents and CVEs, and mentors researchers. The role focuses on identity attacks involving Active Directory, Entra ID, Okta, PAM, and cloud IAM.

Own the strategy, roadmap, development, and continuous improvement of Sanofi’s ServiceNow CMDB, Discovery, Service Mapping, ITOM, and configuration data ecosystem. Lead developers and data specialists while architecting integrations with Snowflake and enterprise platforms using APIs, MCP, automation, AI, and DevOps practices. Drive CMDB maturity, CSDM alignment, data quality, asset management, governance, and hybrid-cloud discovery. Partner with architecture, infrastructure, security, compliance, and AI teams to deliver scalable, AI-ready platform capabilities.

Supports semiconductor inspection products through customer demonstrations, product characterization, troubleshooting, training, documentation, and technical collaboration. Investigates manufacturing yield issues, develops test plans, evaluates tool and algorithm performance, communicates product value, and supports beta testing, tool ramp, and customer adoption. Requires frequent global customer interaction and more than 50% travel.

Leads enterprise architecture for scalable, multi-tenant SaaS systems and cloud-native platforms. Owns AWS architecture, distributed data pipelines, cost optimization, security, governance, and architectural standards. Drives GenAI and machine learning adoption across the software development lifecycle, builds self-service capabilities, guides engineering teams, conducts prototypes, and leads architecture reviews. Requires deep hands-on cloud, distributed systems, coding, infrastructure automation, AI/ML, and strategic technology leadership experience.

Leads and mentors full-stack engineers while remaining hands-on with scalable web, backend, cloud, and AI-enabled application development. Owns architecture through deployment, builds CI/CD pipelines, integrates LLM and RAG capabilities, and ensures application security, performance, availability, and reliability. Responsibilities also include code review, engineering standards, cloud optimization, production monitoring, troubleshooting, cross-functional collaboration, and delivery of business-critical features.

Lead the architecture and development of scalable software for semiconductor manufacturing automation. Build across frontend, backend, databases, messaging, infrastructure, Kubernetes, and AI-powered systems. Partner with product teams, customers, and domain experts to improve manufacturing productivity, quality, throughput, and reliability. Mentor engineers, guide technical direction, improve observability and performance, and influence product decisions through strong engineering judgment and semiconductor manufacturing expertise.

Develop and deploy AI-powered applications using machine learning, generative AI, LLM APIs, prompt engineering, and RAG. Build Python APIs with FastAPI or Flask, process structured and unstructured data, integrate vector databases and embeddings, and support cloud and Docker deployments. Collaborate using Git, REST APIs, and Jupyter Notebook.

Leads clinical systems teams and oversees RPA, system integration, and AI/ML projects. Ensures study goals, timelines, quality standards, client requirements, and contractual obligations are met. Provides technical support, escalation management, contingency planning, resource utilization oversight, and people leadership, including coaching, performance management, interviewing, and mentoring. Supports customer relationships and delivery teams while managing junior professionals.

Leads junior data science teams, translates business needs into technical solutions, selects algorithms, monitors implementation, validates performance, ensures data science best practices, oversees quality assurance and documentation, and presents results to business stakeholders. Requires expertise in Python, SQL, machine learning, statistics, generative AI, cloud and MLOps, along with domain knowledge, business communication, and leadership.

Develops and validates data science algorithms and analytics solutions from requirements gathering through production deployment. Responsibilities include analytical problem formulation, data preparation, model development, risk analysis, design reviews, software development, fleet-level validation, documentation, stakeholder collaboration, customer handover, and deployment support. The role requires adherence to engineering standards, project requirements, review processes, and production release procedures.

The AI Data Scientist will analyze data using machine learning to provide insights for decision-making and improve operational efficiency, as well as lead and mentor teams in AI initiatives.

Leads the technical direction and delivery of production machine learning projects. Designs scalable training, testing, deployment, monitoring, and MLOps pipelines on GCP; mentors engineers and data scientists; collaborates with product and data teams; develops APIs; optimizes model performance; and applies security, privacy, and ethical AI practices.