Top Machine Learning Jobs in India

The Senior Data Engineer will design and optimize scalable data pipelines, implement real-time streaming pipelines, collaborate with data scientists, and oversee CI/CD and data quality practices.

The Senior Technology Engineer will design, deploy, and optimize AI-driven solutions across on-premises and cloud, focusing on scalable inference systems and MLOps.

The Senior Data Scientist will design and maintain data pipelines, optimize machine learning models, and collaborate with cross-functional teams to deliver impactful data-driven solutions.

The ML QA Lead ensures quality in Cerebras software across ML workloads, focusing on testing accuracy, performance, and usability. Responsibilities include driving software quality, automating workflows, debugging issues, and collaborating across teams.

As a Sr. Python AI/ML Developer, you will design systems connecting LLMs with data workflows, focusing on intelligent file processing and metadata reasoning, while building clean API interfaces and ensuring performance optimization.

Lead AI projects by translating business needs into technical solutions, developing AI applications, and integrating AI capabilities across platforms. Oversee large-scale digital transformation initiatives while mentoring teams and ensuring efficient SDLC processes.

The Associate Architect will lead the development and deployment of machine learning solutions on Azure, manage the ML lifecycle, and optimize existing models while providing technical leadership to project teams.

The Lead AI/ML Engineer will design, implement, and govern enterprise AI/ML solutions while partnering with business stakeholders and ensuring compliance with governance standards.

The role involves developing data-driven applications and APIs using Python, building chatbots with LLMs, and deploying scalable solutions.

As a Machine Learning Platform Engineer at Guidewire, you'll architect and scale a secure, cloud-native ML platform, enhancing the ML lifecycle from data ingestion to model monitoring, while collaborating with cross-functional teams for efficient solutions.

Develop and optimize machine learning infrastructure and models for ad technology. Collaborate with teams for data analysis and engineering.

The AI/ML Engineer will design, develop, and deploy machine learning models, utilize various learning techniques, and enhance model performance through data processing. They will work with cloud platforms and CI/CD pipelines while leveraging MLOps practices.

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, train, and deploy NLP and ML models for classification, semantic scoring, and safety. Build containerized inference services, optimize CPU-only deployments, implement evaluation and observability pipelines, and integrate ML outputs with backend systems. Contribute to testing, red-teaming, and hybrid ML-rule systems to ensure reliable production AI.

Design, develop, and deploy machine learning applications focused on embeddings and vector databases. Collaborate with teams to integrate AI/ML solutions and maintain data quality throughout the lifecycle, ensuring scalability and performance optimization of models.

As a Senior Machine Learning Engineer, you'll design, develop, and deploy advanced AI models, mentor junior members, and collaborate across teams to enhance AI capabilities in the construction industry.

As a Senior Machine Learning Engineer, you'll build LLM-powered agents, collaborate on experiments, support prompt engineering, and monitor AI features in production.

Implement and deploy Python-based backend services and integrations for enterprise customers, build data pipelines for AI/ML workflows, integrate LLM/NLP and RAG components, ensure reliable cloud deployments, troubleshoot production issues, and collaborate with Customer Success, ML, and engineering teams to deliver scalable, productionized solutions.

Lead architecture and implementation of an agent-native, AI-powered business applications platform for ad revenue. Build end-to-end pipelines from signals to model inference, recommendations, and validated outputs. Evaluate LLMs, multimodal and multi-agent systems; deploy production ML with serving, CI/CD, monitoring, explainability, and human-in-the-loop guardrails. Partner cross-functionally to iterate and drive revenue-impacting technical decisions.

Develop and scale data management systems for AutoML and forecasting platforms, ensuring data reliability and implementing SLAs across pipelines.

Lead the development of AI subsystems in healthcare, translating requirements into technical strategies for scalable AI/ML and computer vision solutions, ensuring quality and compliance.

Assist in building scalable data workflows, support AI/ML projects, and contribute to mentor-led development and research on GCP features.

Build and deploy production-grade ML and LLM systems across recommendation, churn, fraud, and behavioral scoring. Create modular ML pipelines, implement deep learning/NLP models, integrate via APIs, monitor drift, optimize for latency/throughput/cost in cloud, and collaborate with Data Engineers while implementing MLOps CI/CD and monitoring.

Lead end-to-end design, development, deployment, and monitoring of computer vision and generative AI systems. Optimize training/inference pipelines, collaborate cross-functionally, and drive scalable production solutions for photography-related vision use cases.

Lead Aftershoot's AI/ML function: define roadmap, drive end-to-end computer vision model development, optimize models for edge/mobile, build and mentor an ML research and engineering team, and translate AI advancements into product features.