Top Machine Learning Jobs in India
Enveritas is seeking an Impact Manager to develop and manage impact projects in the agricultural sector. The candidate should have 5-10 years of professional experience, including time in a leading strategy/business consulting firm. Responsibilities include project identification, impact evaluation, program design, partnership negotiation, and team management.
As a Senior Data Scientist, you will design, develop, and deploy AI models in Retail Banking, leading a team of data scientists to deliver high-quality solutions using machine learning and statistical modeling. You'll create chatbots, analyze data for risks, and collaborate with stakeholders to implement AI solutions that meet their needs while mentoring junior scientists.
The AI Technical Program Manager will lead the development and implementation of AI solutions, managing projects from inception to completion while ensuring timely delivery, budget adherence, and alignment with business objectives. Responsibilities include program planning, execution, risk management, stakeholder communication, and driving Agile methodologies within the team.
The Senior Manager of Data Science will develop and enhance data products and machine learning models for risk management, fraud prevention, and collections. Responsibilities include creating monitoring tools, building deployment platforms, and leading collaborative projects to drive business value using alternative data to optimize customer experience and marketing strategies.
The Manager of Data Science will support Visa's clients in India and South Asia by delivering data-driven strategies and solutions. Responsibilities include developing predictive models, managing data science engagements, presenting insights to clients, ensuring quality control of deliverables, and optimizing benchmarking dashboards.
The Senior ML Scientist will design and implement end-to-end ML solutions, executing experiments, maintaining services, and managing project lifecycles. They will leverage expertise in machine learning, statistics, and deep learning to mentor team members and drive innovative projects impacting the sports industry.
As an Analytics & Data Science Sales Engineer at NICE Actimize, you'll develop and implement customized POCs for financial crime detection, leveraging machine learning and analytics. Collaborate with clients and internal teams to tailor solutions, conduct technical demonstrations, and enhance client engagement to drive sales success in a competitive SaaS environment.
The Data Science Manager at Micron Technology will lead efforts in smart manufacturing through data science strategies and applications. Responsibilities include developing smart manufacturing applications, providing technical consulting, solving application issues, and keeping updated with data science trends. The role also involves working with teams to build machine learning models and integrating them effectively into existing systems.
As a Threat Hunter Analyst, you will develop and implement AI-driven solutions for cybersecurity, focusing on threat detection and the automation of threat intelligence processes. Your role includes creating training datasets, refining AI algorithms, overseeing SIEM use cases, and enhancing detection capabilities based on emerging cyber threats.
The Principal Engineer AI/ML at BrowserStack will design and develop data pipelines and AI models, optimize existing systems, lead AI initiatives, and uphold ethical AI practices while mentoring junior engineers.
The Architect will collaborate with AI professionals to implement digital transformation initiatives, design AI architecture, select technologies, ensure compliance with regulations, lead audits, mentor engineers, and enhance operational efficiency using AI/ML.
As an AI/ML Architect, you will design and implement advanced AI/ML solutions for various clients. Key responsibilities include developing architecture designs, leading project deployment, and evaluating AI/ML technologies. You'll mentor junior team members and stay updated on industry advancements while collaborating with sales teams to create AI/ML service offerings.
The Senior Data Scientist is responsible for driving data strategy by leveraging advanced analytics, leading projects in Machine Learning and Generative AI, collecting and preprocessing data, performing statistical analysis, collaborating with engineering teams, and mentoring junior scientists to provide actionable insights that drive business value.
The Sr Software Engineer will design algorithms for automotive applications, focusing on advanced driver assistance systems and autonomous driving features. Responsibilities include developing mathematical models, collaborating with teams, validating algorithm performance, ensuring compliance with industry standards, and implementing CI/CD practices for software maturity and reliability.
The AI/ML Architect will design and develop classical and deep learning algorithms for enterprise cybersecurity solutions. Responsibilities include creating statistical models, applying AI techniques, building production-quality systems, and collaborating with various teams on data engineering and infrastructure.
The Data Engineer 2 is responsible for designing and maintaining data architecture, developing frameworks for data ingestion and quality control, and ensuring data integrity across various platforms. They collaborate with data analysts to align data structures with business needs while leveraging technologies for structured and unstructured data management, both on-premises and in the cloud.
The Lead Technical Program Manager at Target is responsible for managing high-impact organizational initiatives, aligning product roadmaps, and ensuring timely delivery of goals. This role involves strategic planning, risk mitigation, cross-functional collaboration, and monitoring program health while serving as a technical escalation point.
As an Engineering Manager for the ML Platform, you will oversee the design and implementation of software infrastructure while managing technical teams. You'll engage in agile processes to ensure deployment, monitor incidents, and work with stakeholders to maintain product roadmaps. You'll also focus on hiring talent and building scalable platforms for user engagement.
The role involves analyzing requirements and designing scalable software solutions, building distributed systems, deploying cloud-native services, and ensuring their operational support. Responsibilities also include taking ownership of services with clear communication on project progression.
The role involves owning the project lifecycle for ML and data platforms, including design, development, and monitoring. Responsibilities include building scalable data and ML solutions, investigating issues, and mentoring engineers. The focus is on developing features in a distributed microservices environment.
Lead the development of machine learning algorithms and data processing systems for analyzing large datasets, monitoring model performance, and collaborating with cross-functional teams to deliver data-driven solutions in the sports technology sector.
Develop and implement machine learning models for improving business processes and outcomes. Responsible for the full ML stack of the product.
The Engineering Manager will lead the design and implementation of ML and data platforms, ensuring high scalability and performance. Responsibilities include managing software infrastructure, overseeing code reviews, stakeholder communication, product roadmap management, incident handling, and building a self-service ML experimentation platform.
You will own the full project lifecycle from design to deployment, investigate issues, build scalable platforms for Big Data and Machine Learning, and mentor engineers to maintain high standards and best practices.
The role involves designing and developing scalable ML and data platforms on cloud infrastructure, utilizing SQL/NoSQL databases, and distributed systems. Responsibilities include software analysis, cloud deployment, production support, and project ownership, focusing on data-driven product innovation and system resilience.
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