Top Machine Learning Jobs in India

Responsible for researching and designing AI algorithms, implementing AI processes, and managing team activities to solve business challenges.

As an AI Expert, you will review and evaluate AI-generated outputs, correct text, analyze multimedia content, and ensure cultural appropriateness and accuracy in the outputs.

The Sr AI Solutions Engineer will update stakeholders on solutions, productize Centific AI offerings, implement NVIDIA models, and develop AI-enabled applications.

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 multiple engineering teams through design, development, and deployment. Define technical strategy and roadmaps, oversee hiring and resource planning, drive engineering best practices and platform modernization, mentor engineering managers, and collaborate cross-functionally to deliver scalable cloud-native solutions.

Lead design, development, and delivery of complex cloud-native systems. Drive end-to-end technical execution, set engineering standards and architecture, mentor engineers, collaborate with product/UX/architecture, troubleshoot production issues, and champion performance and system improvements.

Lead roadmap and delivery of retail application features that improve dealership workflows, usability, automation, and reporting. Translate dealer pain points into product requirements, partner with Engineering and Design to build scalable experiences, measure and prioritize using data and feedback, and support launch, enablement, and iteration. Contribute to long-term strategy and prepare systems for AI/automation.

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.

Lead and technically drive Nike's multi-cloud platform engineering: design, build, operate, and govern cloud infrastructure solutions (Kubernetes, control plane, CRDs, autoscaling). Split time between hands-on engineering and leadership—shaping technical priorities, best practices, partnering cross-functionally, and mentoring cloud engineering teams.

Build and deploy explainable machine learning systems at enterprise scale, including NLP, LLM, retrieval, ranking, recommendation, prediction, optimization, and multi-agent solutions. Own ML initiatives from data exploration through production monitoring and iteration. Partner with Product, Go-to-Market, and customers to turn ambiguous problems into shipped AI capabilities. Improve ML platform scalability and reliability, communicate technical concepts clearly, mentor engineers, and contribute to engineering excellence.

Develop and support machine learning models and data pipelines for a programmatic DSP, collaborate with cross-functional teams to integrate models into production, analyze new data sources, maintain reproducible experiments, and help evaluate tools and methodologies.

Leads the design, development, and deployment of AI/ML solutions for lending and transaction banking, including credit scoring, fraud detection, NLP, LLMs, computer vision, KYC, and transaction monitoring. Architects scalable deep learning systems, manages the machine learning lifecycle, collaborates with stakeholders, and ensures enterprise integration, performance, and robustness.

Lead the design, development, deployment, and optimization of AI/ML solutions for lending and transaction banking. Build deep learning, NLP, LLM, and computer vision models for credit scoring, fraud detection, document processing, KYC, and transaction monitoring. Collaborate with stakeholders, integrate models into enterprise systems, and ensure scalable, robust machine learning lifecycle management.

Lead the development of AI subsystems in healthcare, creating strategies, optimizing algorithms, and overseeing implementation and deployment. Collaborate with cross-functional teams and manage technical risks.

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.

Train and deploy small, task-specific ML models for realtime English conversational-skill applications. Create datasets and task-specific evaluations, improve internal SOTA, develop ASR and speech-feedback metrics, iterate experiments, ship production systems, and collaborate cross-functionally while helping hire as the team grows.

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 deploy Generative AI solutions, including fine-tuned LLMs, autonomous agents, RAG applications, multimodal systems, and machine learning pipelines. Develop agent architectures for autonomous decision-making and multi-agent collaboration, use vector databases and LLM APIs, integrate AI into chatbots and automation tools, and research emerging methods in Generative AI, NLP, and autonomous systems.