Top Machine Learning Jobs in India

Lead architecture and engineering for cloud-native, data-intensive AI applications. Design scalable, secure solutions, drive MLOps/LLMOps, CI/CD, model deployment/monitoring, generative AI integrations (LLMs, RAG, embeddings), establish observability and governance, mentor teams, and partner with product, security, and business stakeholders.

Design, build, and deploy simulation, optimization, and AI/ML solutions for container terminal operations. Develop operational and strategic models, collaborate with stakeholders, measure impact, and iterate to improve terminal efficiency globally.

Lead SRE for the Network Assurance Data Platform responsible for designing, building, and optimizing cloud and big-data infrastructure for ML/AI workloads. Provide technical leadership, troubleshoot production issues, implement SRE practices (monitoring, alerting, error budgets), mentor engineers, collaborate with cross-functional teams, and drive the platform roadmap and automation for enterprise-scale deployments.

Lead design and delivery of scalable ML and generative AI solutions end-to-end. Set technical direction, build prediction and optimization models, productionize models via MLOps, contribute to analytics platforms, mentor engineers, and collaborate with stakeholders and upstream/downstream teams to deliver business impact.

Lead design, deployment, automation, monitoring and maintenance of production ML models and pipelines. Partner with stakeholders to translate prototypes into production, ensure robust model monitoring/retraining, and manage multidisciplinary teams delivering complex ML projects.

Research, prototype, and integrate AI/ML techniques (GenAI, RL, GNNs, CNNs) into VLSI CAD flows to automate and improve ASIC/SoC design, implementation, and verification. Collaborate with architecture, IP, design, verification, and implementation teams to pilot solutions on real designs and drive process improvements for power, performance, area, and verification quality.

Develop and prototype AI/ML-driven automation solutions for VLSI CAD to improve SoC/IP power, performance, area, and verification. Collaborate with architecture, design, verification, and implementation teams to pilot tools and integrate state-of-the-art techniques (GenAI, RL, GNN, CNN) into ASIC design flows and automation.

Lead R&D and prototype development integrating AI/ML techniques into VLSI CAD flows to improve SoC/IP power, performance, area, and verification. Collaborate with architecture, IP, design, verification, and implementation teams to pilot automation and productionize solutions on advanced-node SoCs.

Research, prototype, and integrate state-of-the-art AI/ML techniques (GenAI, RL, GNNs, CNNs) into VLSI CAD flows to automate and improve ASIC/SoC design, verification, and implementation quality. Collaborate with architecture, IP, SoC, verification, and design enablement teams to pilot solutions on real designs and enable automated flows for advanced-node SoCs.

Design, develop, and deploy AI/ML models for clinical decision-making and operational efficiency, integrating AI capabilities into SaaS platforms.

Design, build, and operate end-to-end ML systems: data pipelines, model development, deployment, monitoring, and optimization. Make architectural decisions, mentor junior engineers, liaise with product stakeholders, and drive scalable, reliable MLOps practices.

Design, develop, and deploy scalable AI/ML solutions: research and implement models (deep learning, transformers, RL), perform data preprocessing and feature engineering, productionize and monitor models, mentor team members, and translate business requirements into technical solutions.

The AI and Machine Learning Engineer develops software algorithms to analyze data, improves system performance using machine learning techniques, and mentors junior team members while collaborating with stakeholders on technical solutions.

Design, develop, deploy, and maintain GenAI-based cloud applications and platforms. Lead solution design, coding, testing, integration and troubleshooting in containerized, Kubernetes-managed environments. Mentor engineers, ensure security and compliance, drive adoption of new technologies, and collaborate across teams to deliver scalable, cloud-native AI/ML services.

Design, develop, and deploy advanced machine learning and deep learning models; lead research, architecture, and productionization of AI solutions; mentor teams; monitor and optimize model performance; collaborate with cross-functional stakeholders to align AI/ML systems with business requirements.

Design, develop, deploy, and monitor production-grade AI/ML and GenAI cloud applications. Build data pipelines, train and evaluate ML/DL models, implement MLOps/LLMOps, integrate LLMs, RAG, and vector search, containerize and deploy services, and collaborate with cross-functional teams to deliver secure, scalable solutions.

Build and maintain React single-page applications and Python backend services with PostgreSQL. Implement APIs (REST/GraphQL), CI/CD (Azure DevOps), shell automation, testing, and SRE practices. Collaborate with product, design, and SRE to deliver reliable production systems, participate in code reviews, troubleshoot incidents, and support occasional extended hours.

Lead ML architecture for document extraction and LLM-powered pipelines. Design scalable production ML systems, set engineering standards, enable RAG/LLM optimization, build feedback loops, mitigate model drift and hallucinations, and mentor senior ML engineers while collaborating cross-functionally to deploy robust Document AI solutions.

Build and maintain MLOps pipelines and CI/CD for ML, deploy models as scalable APIs/microservices, monitor performance and data drift, develop ETL for healthcare data (FHIR, HL7), maintain feature stores, integrate ML outputs with backend systems, ensure code quality, containerize workloads with Docker/Kubernetes, and follow HIPAA/HITRUST compliance.

Design and develop end-to-end AI capabilities in NLP, optimize ML models and systems, and collaborate with cross-functional teams.

Research, prototype, and implement generative and ML algorithms; design, develop, deploy, and maintain Models-as-a-Service; lead MLOps best practices and mentor engineers; collaborate across Data, Cloud, and Architecture teams to deliver model deployments on AWS and GCP.

Design, build, and deploy ML and generative models as services across AWS and GCP. Lead model lifecycle, MLOps, CI/CD, IaC, data pipeline architecture, and mentor junior engineers while collaborating with data, cloud, and business partners.

Develop and optimize machine learning models, manage project priorities, and improve real-world solutions using advanced ML techniques.

As an AI/ML Engineer, you will develop machine learning models, collaborate with teams, preprocess data, and optimize ML pipelines. Additionally, you will mentor junior members and contribute to AI/ML strategies.

The role involves designing and implementing production ML systems, mentoring junior engineers, and establishing best practices for ML performance and evaluation.