Top Machine Learning Jobs in Pune

Develop and deploy machine learning, deep learning, and computer vision solutions for image classification, object detection, segmentation, OCR, and video analytics. Prepare and annotate datasets, optimize models for production performance, collaborate on system integration, and document architectures and deployment workflows. The role also involves staying current with AI/ML research and may include real-time video processing, edge deployment, 3D vision, SLAM, and AR/VR technologies.

Design and implement a unified orchestration layer coordinating vendor and internal LLMs and human agents. Build production-grade Python services, drive ML projects from design to delivery, ensure secure, scalable AI-enabled customer experiences, and collaborate across teams to meet security, compliance, and performance standards.

Design, develop, and deploy machine learning and computer vision models (classification, detection, segmentation, OCR, video analytics). Preprocess and annotate image/video datasets, optimize models for performance and scalability, integrate solutions into production, and document architectures and workflows.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

The role involves developing enterprise applications using Java and Spring, integrating AI features, mentoring junior developers, and collaborating with cross-functional teams.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Lead design, development, and deployment of production-grade LLM and RAG-based AI solutions. Improve retrieval performance, build automated evaluation and validation frameworks, instrument pgvector retrieval KPIs, integrate metrics into CI/CD, and mentor mid-level engineers while driving technical reviews and best practices.

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.

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.

Design and implement AI models, operationalize ML models, develop CI/CD pipelines for ML workflows, and provide technical guidance while ensuring data security compliance.

Senior technical leader defining evaluation-driven development, modeling strategy, MLOps and platform direction on AWS. Oversees multiple agile release cycles, ensures measurable business impact, mentors data scientists, sets governance for evaluation, synthetic data, validation, cost and compliance across the ML portfolio.

Create and review realistic, locale-specific robotics and natural sciences tasks in Marathi; apply scoring rubrics; review and score AI and human responses; produce gold-standard solutions and expert feedback.

As an AI Data Expert, you'll evaluate AI-generated outputs, review text, analyze multimedia, and provide structured feedback based on cultural standards.

Create, adapt, and score domain-specific robotics tasks and rubrics in Telugu to evaluate AI systems; review submissions for accuracy, regulatory alignment, and produce gold-standard solutions and expert feedback.

Create and review realistic, domain-specific robotics tasks in Hindi; apply scoring rubrics; evaluate AI and human responses for accuracy, regulatory alignment, and professional quality; and produce gold-standard solutions and expert feedback.

Create and review realistic, locale-specific robotics and natural sciences tasks in Tamil to evaluate AI systems; apply scoring rubrics, review and score submissions for accuracy and regulatory alignment, and produce gold-standard solutions and expert feedback.

The Senior Manager, Machine Learning leads the Conversation Memory team, focusing on developing foundational AI features, ensuring operational excellence, mentoring engineers, and managing project timelines for ML solutions.

Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.

Embed with customers to understand their technical environments, build and deploy custom integrations and workflows, write production code, prototype and scale solutions, debug complex issues, and translate customer insights into product requirements. Partner with sales and customer success on technical evaluations, pilots, and POCs while identifying broader platform opportunities. The role requires customer travel of approximately 20–50%.

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

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

Lead design and production of computer vision biometric systems (face detection, quality, attributes, recognition). Build/train models, implement fairness benchmarking, own end-to-end ML pipelines on AWS, optimize for low-latency inference, and mentor engineers.

Lead end-to-end ML systems: data pipelines, model development (transformer-based detection), sim-to-real synthetic data, multi-sensor fusion, on-device real-time inference (Jetson), MLOps (experiment tracking, versioning), and production deployment and monitoring. Set ML roadmap and hire/lead as the team grows.