Top Machine Learning Jobs in India

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.

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.

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.

Build and maintain Python backend services and internal tools for AI/LLM applications. Integrate LLM APIs, design scalable REST APIs and async workflows, handle prompt management and model routing, monitor performance, debug distributed systems, automate data workflows, and collaborate to productionize AI features with emphasis on scalability, reliability, and safety.

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.

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

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.

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.

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.

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.