Top Machine Learning Jobs in India

Build and deploy production-grade AI/ML systems spanning LLMs, RAG, agentic AI, computer vision, edge AI, and autonomous robotics. Responsibilities include model fine-tuning, distributed system architecture, GPU optimization, simulation-based robotics development using MuJoCo and Isaac, autonomous driving simulation, and end-to-end algorithm deployment. The role requires translating business problems into scalable AI products and contributing to commercialization and product-market fit.

Build and deploy Generative AI solutions, including fine-tuned LLMs, autonomous agents, RAG applications, multimodal systems, and machine learning pipelines. Develop agent architectures for autonomous decision-making and multi-agent collaboration, use vector databases and LLM APIs, integrate AI into chatbots and automation tools, and research emerging methods in Generative AI, NLP, and autonomous systems.

Build and operate scalable MLOps infrastructure for training, deployment, monitoring, governance, and continuous improvement of production machine learning models. Develop CI/CD, model serving, feature store, experiment tracking, model registry, and automated deployment capabilities. Optimize Apache Spark pipelines, support LLM and agentic AI workflows, improve platform reliability and observability, and partner with data science, platform engineering, and product teams.

Own the vision and multi-quarter roadmap for Rubiscape’s AutoML, MLOps, and agentic AI products. Translate enterprise AI needs into measurable features, evaluate emerging AI techniques, conduct discovery with technical and business users, and lead responsible AI governance capabilities. Partner with research, marketing, and pre-sales on experimentation, go-to-market, positioning, demos, competitive differentiation, and model/API versioning for regulated enterprise deployments.

Build, train, evaluate, and operationalize machine learning models for Rubiscape’s AutoML and MLOps platform. Responsibilities include feature engineering, data quality collaboration, MLflow-based experiment tracking and model versioning, production deployment, and monitoring for drift, bias, and performance degradation. The role requires production-quality Python development and collaboration across data and platform engineering teams, with opportunities to prototype applied machine learning approaches.

Design, develop, deploy, evaluate, and optimize classical machine learning and deep learning models. Build end-to-end ML pipelines covering data preprocessing, feature engineering, training, validation, and performance analysis. Apply models to structured and unstructured data, including image classification and object detection. Collaborate with data engineers and software developers, optimize solutions for scalability and efficiency, and document experiments and results.

Develop, deploy, and scale AI/ML and generative AI solutions for a fintech platform. Design data models and pipelines, build agentic frameworks, integrate OCR and vector DBs, implement RAG, and champion MLOps and cloud deployment practices while mentoring teammates.

The role focuses on designing AI-driven workflows, developing user-friendly interfaces for complex backend logic, and enhancing UI/UX with real-time interactions and performance optimizations.

Design, deploy, and operate end-to-end MLOps pipelines and cloud-native infrastructure. Build CI/CD for model training and deployment, containerize workloads with Docker/Kubernetes, implement IaC with Terraform, monitor model performance and drift, optimize inference latency, and collaborate with data scientists and engineering teams to integrate ML into production systems.

Design, develop, and deploy enterprise AI/ML solutions including GenAI, LLM-based RAG systems, and agentic AI. Build scalable models, integrations, monitoring, and reusable frameworks while collaborating with stakeholders for production adoption and continuous improvement.

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.

Develop and deploy machine learning solutions on Azure Databricks, managing the entire ML lifecycle and collaborating with cross-functional teams to address business challenges.

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, 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.

Design, develop, and deploy production-grade AI/ML and GenAI solutions across the ML lifecycle. Build models, data pipelines, LLM/RAG/agent applications, implement MLOps/LLMOps, monitor performance, use vector DBs and observability tools, collaborate with cross-functional teams, and maintain secure cloud-native AI services.

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

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.