Top Machine Learning Jobs in India

Implement and deploy Machine Learning solutions, collaborate on AI/ML opportunities, develop production-grade models, and enhance customer experience through innovation and performance monitoring.

Designs, builds, and optimizes machine learning models and infrastructure, develops production-grade ML services and data pipelines, conducts research and evaluation, implements LLM fine-tuning and RAG solutions, and partners cross-functionally to translate business requirements into deployable ML deliverables.

The Senior ML Research Engineer will develop computer vision algorithms for item authentication, mentor team members, and oversee model deployment.

As a Data/ML Engineer, you will design and implement data pipelines, optimize workflows for unstructured data, and integrate LLM frameworks to power analytics and BI solutions.

The AI/ML Engineer will build and deploy GenAI applications, design agentic systems, develop AI backend services, and ensure responsible AI compliance.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Design, develop, and deploy deep learning and computer vision models for fraud detection (including deepfake and synthetic data detection). Own production models, research new algorithms, deploy on AWS SageMaker or devices, monitor model performance, and collaborate with engineers and product managers in an Agile environment.

The Data Engineer will design, build and maintain data pipelines, optimize performance, enforce data governance, and collaborate with teams to support model development.

Design, build, and deploy ML models for personalization and recommendations. Collaborate with teams, ensure performance, and mentor junior engineers.

Design, implement, and scale AI/ML solutions using statistical and deep learning techniques. Conduct research, architect models, optimize performance, mentor junior engineers, collaborate with cross-functional teams, and translate business requirements into production-ready systems and insights.

Join the Flight Physics FARM core team to prototype and validate AI models (LLMs, CNNs, surrogates), build MLOps/POD workflows, partner on V&V for certification, support aerodynamic engineering, improve data governance and ETL, mentor teams, manage suppliers, and drive industrialization of high-impact prototypes.

Architect scalable, low-latency AI/ML systems and real-time inference pipelines for voice, vision, and NLP applications. Lead model productionization, infrastructure and framework selection, architecture reviews, optimization, observability, and MLOps best practices. Collaborate with product, data science, and engineering teams while mentoring engineers. The role requires expertise in distributed systems, LLM inference optimization, vector databases, RAG, containerization, cloud platforms, CI/CD, edge deployment, and software architecture.

Develop and deploy deep learning and reinforcement learning systems for urban air mobility, including routing and scheduling tools for aircraft networks. Build data and training pipelines, productize research prototypes, optimize inference performance, evaluate model behavior, and deploy AI tools to engineering teams. Responsibilities also include designing reliable production ML systems, constructing simulation environments, engineering rewards, and operating multi-step agentic workflows.

Develops and deploys machine learning tools across engineering disciplines, including LLM, multimodal, and generative AI systems. Responsibilities include building training pipelines, productizing research prototypes, benchmarking models, creating evaluation methodologies, and deploying AI tools through structured pilots. The role requires experience with PyTorch, TensorFlow, RAG pipelines, vector databases, retrieval optimization, citation grounding, and containerization. Preferred experience includes reinforcement learning, distributed processing, multimodal models, local GenAI deployment, and agentic workflows.

Entry-level role for fresh graduates to support data collection, cleaning, exploratory analysis, and implementation of machine learning models. Collaborate with a data science team, document findings, and participate in training while working from the Pune office during initial onboarding.

Design and implement AI models, operationalize ML models, develop CI/CD pipelines for ML workflows, and provide technical guidance while ensuring data security compliance.

Leads end-to-end machine learning research, including problem definition, experimentation, model development, evaluation, and production deployment. Develops advanced models and scalable training pipelines using modern frameworks, collaborates with ML Engineering, MLOps, Product, and Data Engineering, and defines datasets and labeling strategies. Mentors junior researchers, reviews technical work, establishes reproducibility standards, and contributes to AI strategy, roadmap planning, and team development.

Lead the design and development of scalable machine learning infrastructure, including training, evaluation, deployment, model serving, feature stores, data pipelines, CI/CD, monitoring, and governance. Optimize production inference systems, ensure reliability and performance, collaborate with research and engineering teams, review architecture and code, and mentor junior ML engineers.

Senior software engineer responsible for designing, developing, and deploying IIoT device and network management features. Lead full lifecycle development, mentor engineers, collaborate cross-functionally, apply AI-first approaches, and ensure scalable, secure, real-time edge and cloud solutions.

Design, implement, and maintain high-quality REST web services for Intuits Connected Identity platform. Hands-on coding (80-95%), resolve defects across SDLC, collaborate with Agile teams, prototype/deploy components on AWS, and apply software design patterns and TDD practices.

Develop and maintain high-performance, scalable risk metrics engines and workflows. Write production-grade backend code, implement cloud-first architectures (AWS), integrate AI-enabled tools, follow CI/CD and testing best practices, and collaborate with quantitative engineers and product managers to support firm-wide risk, liquidity, and trading analytics.

Lead end-to-end delivery of large cross-functional technical programs, coordinate product/engineering/infrastructure/security stakeholders, manage risks and dependencies, establish status cadences and executive updates, mentor junior TPMs, and influence strategic roadmaps to meet product milestones.

Designs and scales AI/ML platforms for pricing, promotion optimization, and demand forecasting across B2C and B2B channels. Develops time-series, causal inference, reinforcement learning, recommendation, and price elasticity models. Builds end-to-end ML pipelines, production API integrations, monitoring, retraining, and drift detection. Collaborates with data engineering, product, marketing analytics, cloud, and business teams to optimize revenue, profitability, promotions, competitive pricing, and customer-segment decisions.

Design, build, and productionize ML models for credit scoring, fraud detection, and segmentation. Maintain model governance and documentation, develop scalable MLOps pipelines and monitoring, generate analytical insights and dashboards, and mentor junior data scientists while leading end-to-end data science initiatives.

Build and own end-to-end AI and machine learning services, integrate trained ML and GenAI models into applications, and collaborate across teams on complex initiatives. The role requires developing AI/ML/LLM modules, working with Python frameworks and data stores, maintaining CI/CD pipelines, implementing event-driven architectures, and applying expertise in LLMs, vector databases, embeddings, prompt and context engineering, and cloud services.