Top Machine Learning Jobs in India

This role focuses on data annotation for machine learning, requiring curation and organization of data for AI training purposes.

The Application Engineer will collaborate with customers in the automotive, aerospace, and energy industries to solve engineering challenges through machine and deep learning, utilizing Python to process data and develop tailored workflows, while providing training and translating customer feedback into product improvements.

As a Technical Account Manager, you will drive customer adoption of AI-driven simulation tools, engage with engineering teams, and support software implementation in design workflows.

Lead research and infrastructure for systematic credit market-making: source and clean credit datasets, engineer predictive features, build alpha models, and develop scalable, AI-driven backtesting and signal platforms. Mentor junior researchers, collaborate with global trading desks, and ensure production-grade, high-performance quantitative tooling.

As a Demo Engineer, you support pre-sales by designing AI-driven demos and proof of concepts, collaborating closely with teams to showcase Cresta's capabilities.

Designs and operationalizes enterprise analytics, KPI frameworks, data validation pipelines, machine learning solutions, and governed GenAI capabilities. Builds advanced SQL and Python data transformations, RAG and agentic AI workflows, evaluation frameworks, and production-ready deliverables. Applies anomaly detection, forecasting, and other advanced analytics techniques while implementing testing, logging, guardrails, deployment packaging, documentation, and security controls.

Develop and productionize generative AI applications, including RAG, multi-agent systems, Text2SQL, and fine-tuned models. Own production rollouts, evaluation, and optimization while partnering with product and engineering teams to shape roadmaps. Apply machine learning and data science expertise to business problems, deploy solutions on cloud platforms, and communicate technical concepts to varied audiences. The role also involves collaboration across time zones and may support healthcare and pharmaceutical analytics use cases.

Build and operate AWS-based MLOps platforms for autonomous-driving workloads. Maintain highly available multi-zone environments, distributed multi-GPU Ray training clusters, and infrastructure for compute, storage, networking, and security. Develop Airflow and MLflow pipelines, support GitHub-based CI/CD for ML code, models, and infrastructure, troubleshoot platform issues, and ensure workflows are reproducible, traceable, and auditable.

Design, develop, and productionize machine learning and GenAI solutions across NLP, computer vision, forecasting, recommendations, and intelligent automation. Build MLOps pipelines and deploy scalable systems across AWS and Google Cloud using services such as SageMaker, Bedrock, and Vertex AI. Partner with customers and internal teams to define measurable ML solutions, improve model performance, establish observability and governance, and mentor engineers while contributing reusable architectures and best practices.

Build, deploy, and optimize machine learning solutions across NLP, OCR, computer vision, forecasting, recommendation, generative AI, and agentic AI use cases. Prepare data, engineer features, train models, integrate APIs, and support production deployments in AWS and GCP. Apply MLOps practices including versioning, CI/CD, monitoring, troubleshooting, and experimentation. Collaborate with engineers, architects, customers, and stakeholders on solution delivery, documentation, demos, and reusable components.

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