Top Machine Learning Jobs in India

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.

Develops and deploys machine learning tools across engineering disciplines, including LLM, multimodal, and generative AI systems. Responsibilities include building training pipelines, productizing research prototypes, benchmarking models, creating evaluation methodologies, and deploying AI tools through structured pilots. The role requires experience with PyTorch, TensorFlow, RAG pipelines, vector databases, retrieval optimization, citation grounding, and containerization. Preferred experience includes reinforcement learning, distributed processing, multimodal models, local GenAI deployment, and 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.

Lead the design and development of scalable machine learning infrastructure, including training, evaluation, deployment, model serving, feature stores, data pipelines, CI/CD, monitoring, and governance. Optimize production inference systems, ensure reliability and performance, collaborate with research and engineering teams, review architecture and code, and mentor junior ML engineers.

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.

Develop and maintain high-performance, scalable risk metrics engines and workflows. Write production-grade backend code, implement cloud-first architectures (AWS), integrate AI-enabled tools, follow CI/CD and testing best practices, and collaborate with quantitative engineers and product managers to support firm-wide risk, liquidity, and trading analytics.

Lead end-to-end delivery of large cross-functional technical programs, coordinate product/engineering/infrastructure/security stakeholders, manage risks and dependencies, establish status cadences and executive updates, mentor junior TPMs, and influence strategic roadmaps to meet product milestones.

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.

Architects, develops, tests, deploys, monitors, and maintains end-to-end AI/ML services. Owns complete modules and features, integrates trained ML and GenAI models, collaborates across teams, and aligns technical roadmaps with target architecture. The role requires strong Python, ML frameworks, LLM, vector database, prompt engineering, cloud, data store, CI/CD, and event-driven architecture experience.

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 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.

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.

Design, develop, and deploy scalable AI/ML and Generative AI solutions (LLMs, RAG, agentic AI). Build AI agents, create intelligent workflows, fine-tune and monitor models, integrate with enterprise systems, and support production deployment and continuous improvement.

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 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 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, build, deploy, and optimize machine learning and deep learning models and production-ready MLOps systems. Develop data pipelines, clean and transform large datasets, engineer features, evaluate model performance, and improve accuracy and efficiency. Integrate AI solutions into products with cross-functional teams, including LLM training and API integrations. Apply expertise in NLP, computer vision, generative AI, cloud platforms, big data, APIs, and microservices.