Top Machine Learning Jobs in India

Lead design and delivery of simulation, optimization, and AI/ML solutions for container terminal operations. Build operational and strategic tools, coach junior team members, liaise with stakeholders, measure model impact, and iterate models to improve efficiency in yard, vessel, and truck operations.

Design, build, deploy, and monitor ML models at scale. Collaborate with DS, QA, infra, and engineering teams to productionize and maintain ML services, optimize production code, and evolve solutions using modern ML practices.

Lead application systems analysis and programming for large-scale data and AI/ML solutions. Partner with management to define enhancements, ensure architectural compliance, develop coding/testing standards, resolve complex issues, coach mid-level developers, and drive operational/process improvements while maintaining regulatory and policy compliance.

Design, develop, and deploy machine learning models for predictive analytics, NLP, and computer vision. Preprocess and structure large datasets, implement scalable production deployments, monitor model performance, and collaborate with cross-functional teams to integrate AI-driven features while researching and applying best practices.

Lead full lifecycle development of L2/L3 embedded RAN protocol stacks: design, coding, unit testing, debugging, and integration. Implement and enhance MAC/RLC features, analyze logs, debug in Linux, and support product releases. Apply AI/ML concepts for RAN optimization and develop/maintain solutions used by global customers.

Lead end-to-end development, deployment, and monitoring of production ML models for a programmatic DSP. Design evaluation frameworks, reduce inference latency, ensure production stability, mentor junior engineers, and coordinate cross-functional rollouts to drive measurable KPI improvements.

Design, train, optimize, and deploy production computer vision and visual search models for mobile and cloud. Build scalable training pipelines, model versioning, evaluation, and inference services. Improve robustness, mobile performance, and retrieval quality while collaborating with mobile, backend, and product teams to integrate and operationalize ML solutions.

Deploy and maintain computer vision models in production: monitor failures, diagnose issues, propose algorithmic or data fixes, curate datasets, validate results for customers, and adapt SOTA architectures into efficient, impactful solutions.

The AI/ML Specialist will design and implement AI/ML models, collaborate with data engineers, ensure model explainability, and communicate technical concepts to stakeholders.

Lead implementation of Python-based backend integrations and data pipelines to deploy LLM/NLP/RAG solutions for enterprise clients. Configure cloud deployments, ensure reliability and observability, troubleshoot production issues, and collaborate with Customer Success, ML, and engineering teams. Mentor juniors and document implementation playbooks.

Lead design and production of face recognition and biometric CV systems. Build, train, optimize models (PyTorch/TensorFlow/JAX), own end-to-end ML pipelines (Airflow), ensure fairness, deploy low-latency inference on AWS, and mentor engineers.

Responsible for designing and building automated test frameworks for AI hardware/software, focusing on validation of machine learning models and CI/CD processes.

Design, develop, and deploy scalable AI/ML and LLM-powered solutions. Build large-scale data architectures, pipelines (Kafka/Flume/Spark), and big-data environments on-prem/cloud. Develop agentic AI systems, optimize model performance and reliability, enable researcher data access, collaborate cross-functionally, and contribute to research, PoCs, and production-grade deployments.

Design, build, train, deploy, and support enterprise-scale AI/ML solutions including LLM-based, multimodal, and geospatial models for document understanding, legal entity extraction, and automatic generation of geo-tagged polygons integrated with GIS platforms.

Lead design, development, evaluation, optimization, and deployment of machine learning and NLP models. Manage data preprocessing and feature engineering, integrate models into products, deploy ML models as Django web applications, and share AI/ML knowledge to improve scalability and performance.

Design, build, and deploy enterprise AI applications powered by LLMs. Develop and optimize multi-agent systems, RAG, and prompt engineering solutions; fine-tune and evaluate models; create reusable Python libraries, scalable APIs/microservices, and integrate AI with enterprise systems and cloud platforms. Provide technical leadership and mentor engineers.

Design, deploy, and operate scalable AI/ML infrastructure and CI/CD for production ML systems. Build and maintain cloud-native, containerized platforms supporting LLM agents, data pipelines, model serving, telemetry, and messaging; ensure performance, reliability, cost optimization, security, and collaboration with ML engineers and data scientists.

Build and ship web-based machine learning features using Python or NodeJS, implement NLP models, integrate and manage data with NoSQL stores (DynamoDB), develop on AWS, work in agile teams, and collaborate cross-functionally to deliver customer-ready product features.

Design, develop, and deploy ML/DL models (classification, regression, recommendation, NLP, CV, generative). Build end-to-end pipelines, experiment with LLMs/foundation models, optimize for production, collaborate with engineers and product teams, and stay current with research.

Design, build, and deploy scalable AI/ML solutions and pipelines (training, validation, deployment, monitoring). Collaborate with product and engineering to turn business problems into models (NLP, recommendations, anomaly detection). Implement MLOps, CI/CD, and production infrastructure for batch and real-time inference while maintaining model governance and performance.

As an MLOps Engineer, you'll enhance R&D infrastructure for ML solutions, ensuring reliability and scalability, while collaborating with diverse teams.

The AI/ML Engineer will build scalable data-intensive AI/ML solutions, develop pipelines, optimize data processing systems, and manage end-to-end AI/ML project lifecycles.

Seeking an AI/ML Engineer to design, develop, and deploy AI and machine learning solutions, using state-of-the-art models and technologies.

The role seeks an AI Lead to drive Generative AI and Computer Vision projects, focusing on precise visual transformations and model training while mentoring engineering teams in best practices.

Lead design, development, and production deployment of enterprise-scale ML and GenAI solutions (LLMs, RAG, recommendation systems). Architect scalable inference and training pipelines, mentor MLEs, collaborate with stakeholders, ensure security/compliance, and measure and optimize model performance and business impact.