Top Machine Learning Jobs in Pune

Responsible for the end-to-end design, development, and deployment of Machine Learning models, collaborating with teams, and managing deployment lifecycles.

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

The role involves designing and deploying machine learning models, ensuring model performance, and optimizing ML infrastructure on GCP, collaborating closely with data engineers and scientists.

Design, implement, and maintain machine learning systems. Collaborate with data experts and transform business problems into data problems through statistical modeling and analysis.

Develop, deploy, and scale AI/ML and generative AI solutions for a fintech platform. Design data models and pipelines, build agentic frameworks, integrate OCR and vector DBs, implement RAG, and champion MLOps and cloud deployment practices while mentoring teammates.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Deploy and productionize ML models on GCP Vertex AI for real-time and batch inference. Build scalable pipelines, perform feature engineering, monitor model performance and drift, support ML-Ops and governance, and collaborate with teams on retail banking (credit risk, marketing, customer service) use cases.

Design and implement AI/LLM-driven data extraction and ETL pipelines, build web crawling and API integrations, implement RAG systems, mitigate LLM hallucinations, evaluate third-party tools, and collaborate with content and analytics teams to deliver high-quality, scalable data for downstream analysis.

Lead design and implementation of enterprise-scale generative AI and agentic systems. Architect multi-agent orchestration, RAG and inference pipelines, fine-tuning/quantization strategies, integrations (APIs, webhooks, connectors), and secure data/model management. Optimize deployments across cloud, edge, and on-premises while establishing evaluation, selection, and lifecycle practices.

The Senior ML Research Engineer will develop computer vision algorithms for item authentication, mentor team members, and oversee model deployment.

The role involves solving customer problems by building ML solutions, engaging with various stakeholders, and taking ownership of the outcome through deployment.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

As a Machine Learning Engineer, you will build and maintain ML infrastructure, develop credit models and collaborate on data-driven features. The role requires extensive hands-on experience in ML engineering within production environments.

The role involves developing and implementing Machine Learning models, managing data pipelines, and supporting Data Science teams with best practices in ML operations and methodologies.

The role involves collaborating with Data Science teams to implement and manage ML models, designing GenAI solutions, and automating ML operations, while establishing best practices in ML lifecycle management.

Design and develop machine learning infrastructure and models, support product teams, build scalable data services, and promote collaboration within a diverse engineering team.

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

Design, build, and optimize ASR and speech-related ML systems, handling real-world audio for production deployment and improvements.