Top Machine Learning Jobs in India

Develops and deploys AI and machine learning models and algorithms in production. Applies NLP, NLU, computer vision, deep learning, speech recognition, statistics, optimization, and probability theory to build practical AI solutions. Analyzes data, investigates complex problems, interprets results, and communicates analytics concepts to leadership and internal stakeholders. Requires an undergraduate degree or equivalent experience.

Leads the design, development, deployment, and maintenance of enterprise AI and machine learning solutions. Responsibilities include Generative AI, NLP, computer vision, deep learning, speech recognition, MLOps, distributed data platforms, model governance, experimentation, and performance optimization. The role translates business challenges into scalable AI applications, partners with cross-functional leaders, communicates insights to executives, mentors team members, and promotes responsible AI practices.

Lead the architecture, automation, deployment, monitoring, and support of highly available identity and access management platforms across public and private clouds. Build infrastructure as code, maintain CI/CD pipelines, execute zero-downtime releases, monitor production systems, integrate analytics and machine learning, and ensure infrastructure, application, and cloud security while coordinating with global teams.

Leads financial crime analytics supporting product and operations teams. Owns metrics, reporting, visualizations, risk and customer-experience frameworks, and analytical discovery of controls, rules, and models to mitigate emerging threats. Partners with product managers, data scientists, engineers, and operations, while managing and developing a team of three analysts. Requires extensive analytics and people-leadership experience, financial crime expertise, SQL, Python or R, and data visualization skills.

Designs and optimizes radio networks, delivering presales consulting and complex end-to-end solutions for global telecommunications customers. The role develops service offerings, gathers customer requirements, implements solutions within scope and budget, and provides technical leadership and knowledge sharing. Expertise is required in 5G, RAN design and optimization, Ericsson systems, performance and configuration management, drive-test tools, data visualization, analytics, and troubleshooting across RAN, transport, core, and devices.

Develops secure, scalable software and data engineering solutions within an agile banking technology team. Responsibilities include coding, troubleshooting, system design, SQL development, processing large datasets, and supporting batch and streaming data platforms. The role requires hands-on experience with Java, AWS, Python, Scala, Spark, data warehousing, SQL, and data analytics, along with responsible use of enterprise AI-assisted development tools.

Lead the design and deployment of production LLM agents and RAG systems that detect, triage, and remediate platform issues. Build orchestration, tool integrations, guardrails, evaluation frameworks, and observability integrations. Partner with SRE, InfoSec, and platform teams to deliver secure, responsible AI capabilities within software development workflows. Optimize models, prompts, retrieval quality, cost, latency, and operational impact.

Build and deploy end-to-end AI and machine learning solutions for smart manufacturing. Responsibilities include developing data pipelines, predictive models, APIs, web applications, LLM and agentic AI integrations, containerized services, and MLOps workflows. The role supports manufacturing automation, robotics, MES integration, cloud deployment, CI/CD, monitoring, model evaluation, Responsible AI, and production optimization while collaborating with data scientists, software engineers, and manufacturing stakeholders.

Design and deploy AI/ML capabilities for Ericsson products, including intelligent automation, operational analytics, generative AI, knowledge management, and developer-assistance solutions. Lead innovation from research through production, influence AI/ML architecture and product strategy, and collaborate with product and engineering teams to deliver scalable, cloud-native systems.

Join Celonis' 12-month Orbit Graduate Program as an Associate Value Engineer to support sales and customers by uncovering process problems, building proofs and AI prototypes on the Celonis platform, quantifying value, presenting to executives, driving change management, and feeding product insights back to engineering.

Leads quantitative analysts in developing and launching benchmark, thematic, factor, strategic beta, and multi-asset indexes. Owns the full index lifecycle, including opportunity identification, signal testing, back-testing, methodology design, risk analytics, and launch. Champions AI-enabled research automation, mentors team members, collaborates across research groups, and develops analytical tools, white papers, and client-ready materials.

Develop and improve aviation operational-efficiency software using machine learning, deep learning, neural networks, Python, and MATLAB. Collaborate with international Agile teams on solution design, architecture, technical roadmaps, and backlog delivery. Support flight management and flight operations applications, reduce technical debt, document systems, research emerging technologies, and recommend product improvements. The role also involves mentoring, stakeholder presentations, cross-functional coordination, and domestic and international travel.

Develop and deploy enterprise-scale machine learning and Generative AI solutions for fraud detection, risk scoring, anomaly detection, and security intelligence. Build scalable ML pipelines, MLOps capabilities, LLM and agentic AI applications, and real-time decisioning systems using large transactional datasets. Collaborate with engineering and data science teams, evaluate emerging AI technologies, contribute to reusable platforms, and support research, patents, explainability, security, and responsible AI practices.

Leads hands-on development and production deployment of AI and machine learning solutions for fraud detection and risk intelligence. Designs scalable ML pipelines, MLOps frameworks, generative and agentic AI systems, and real-time decisioning capabilities using large-scale transactional data. Provides technical leadership, evaluates emerging technologies, mentors engineers, establishes engineering standards, and translates applied research into secure, explainable, reliable production systems.

Architect and deliver scalable cloud, hybrid, and on-premises data analytics, AI, machine learning, and data science solutions for smart manufacturing. Lead architecture decisions, establish data and platform standards, troubleshoot complex technical issues, collaborate across technical teams, and mentor data scientists, AI engineers, and product owners. The role requires extensive data technology experience, hands-on cloud AI/ML architecture expertise, software engineering skills, and strong stakeholder communication.

Design and implement ML solutions (predictive models, recommender systems, anomaly detection, deep learning). Optimize technical requirements, build cloud and on-prem ML pipelines, collaborate with business stakeholders, and mentor junior data scientists.

Perform end-to-end system verification of Ericsson 5G RAN solutions, including capacity, performance, scalability, mobility, and high-load traffic testing. Analyze logs, traces, KPIs, and protocol-layer behavior; troubleshoot PHY/MAC/RLC/PDCP/IP issues and identify root causes. Support feature integration, test automation, verification frameworks, and CI/CD improvements. Collaborate with development, integration, CI, and product teams while applying modern and AI-assisted verification methods.

Develops innovative investment indexes from ideation through launch, conducting quantitative research, portfolio construction, validation, and analytics. Collaborates with research and product teams, applies statistical and machine learning techniques, and creates white papers, factsheets, presentations, and client materials. The role requires strong investment knowledge, programming and SQL proficiency, communication skills, and experience in quantitative finance or related analytical fields.

Leads client-facing data science engagements for short-term lending and credit risk. Performs descriptive, predictive, and prescriptive analytics using segmentation, regression, machine learning, scenario analysis, and large-scale data processing. Develops models and monitoring programs in R, Python, SQL, Spark, and cloud platforms. Presents recommendations, supports risk strategy and growth initiatives, mentors junior colleagues, manages multiple projects, and evaluates emerging analytical techniques and technologies.

Leads the development, deployment, validation, documentation, and monitoring of complex fraud risk models using statistical and machine learning methods. Analyzes large structured and unstructured datasets, develops predictive features, evaluates fraud patterns, applies graph and anomaly analytics, and supports model governance. Partners with risk, fraud, operations, product, and technology teams to translate findings into recommendations, manage initiatives, defend methodologies, and mentor data scientists and analysts.

Design, develop, deploy, and support AI-powered applications for smart manufacturing. Build end-to-end machine learning systems, data pipelines, APIs, web applications, automation tools, and predictive models. Integrate LLMs, agentic AI, robotics, MES, and industrial systems. Develop containerized applications with Docker and Kubernetes/OpenShift, contribute to CI/CD and MLOps practices, and apply Responsible AI, monitoring, testing, and model governance principles.

Own the APAC onboarding and KYC product strategy, roadmap, and delivery across multiple markets. Lead identity verification, localization, regulatory controls, and financial-crime mitigation initiatives while balancing conversion, customer experience, operational costs, and compliance risk. Partner with engineering, design, analytics, operations, risk, and compliance teams to ship measurable customer-facing outcomes, improve verification and onboarding metrics, and generalize successful capabilities across Wise.

Leads software engineering initiatives within finance technology, delivering secure, scalable Java and React applications on AWS. Responsibilities include system design, coding, testing, troubleshooting, code reviews, automation, operational stability, vendor and architecture evaluations, and multi-team adoption of AI-assisted development tools. Establishes governance, security, resiliency, measurement, and responsible AI practices while coaching engineering teams and driving technology communities of practice.

Owns delivery structure for the SONIC client analytics product, translating client service needs into metrics and analytics outcomes. Responsibilities include requirements gathering, data taxonomy, process and risk analysis, stakeholder coordination, scope management, testing, prototyping, and delivery validation. The role partners with business, operations, design, data science, artificial intelligence, machine learning, and technology teams to improve service performance and client experience measurement.

Leads architecture and development of secure, scalable cloud-native applications using Java, Spring Boot, microservices, Kubernetes, and AWS. Defines target-state architecture, evaluates emerging and AI-enabled technologies, guides technical teams, reviews code, and improves system reliability. Applies distributed systems patterns, data architecture, CI/CD, observability, security, and governance practices while influencing business and technology stakeholders in a regulated financial-services environment.