Top Machine Learning Jobs in Pune

Lead the design, development, deployment, and optimization of AI-driven applications, especially RAG-based chatbots and generative AI solutions. Build scalable systems with Python and Java, deploy them on AWS and Azure, and apply machine learning, deep learning, NLP, vector databases, embeddings, and LLM integrations. Collaborate with cross-functional teams, manage technical deliverables, mentor engineers, ensure quality through testing and optimization, and adopt emerging AI technologies.

Lead design and delivery of RAG-based chatbot and AI solutions: architect scalable Python/Java systems, deploy and manage on AWS/Azure, mentor engineers, ensure quality, and research emerging AI methods.

Lead development and deployment of ML/AI solutions: build models (classification, prediction, anomaly detection, NLP), prototype ML products, prepare and pipeline data, collaborate with product and engineering, deliver insights, and mentor team members.

Lead development and production deployment of AI/ML models focused on personalization, recommendations, segmentation, and predictive analytics. Collaborate with data engineering and development teams to ensure data readiness, integrate models into production (real-time systems like Kafka), apply AIOps to monitor performance, and mentor an AI/ML team.

Design, develop, and productionize machine learning and deep learning models, build scalable data and embedding pipelines, implement RAG/LLM-based applications using LangChain/LlamaIndex, integrate models as APIs, and optimize model performance and scalability for product integration.

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.

Senior Databricks MLOps Engineer responsible for automating the end-to-end ML lifecycle: CI/CD, packaging, deployment, scheduling, monitoring, and reproducible experiments. Build frameworks and pipelines, integrate embedding/LLM components (RAG, vector DBs, LangChain), optimize ML workloads, and collaborate with data scientists and platform teams to productionize models for Claims Payment Integrity.

As a Junior-Midlevel Developer, you will develop, maintain, and enhance an AI-powered education platform, collaborating with teams to build software and optimize performance.

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

The ROR Developer will focus on software engineering practices, design, data structures, algorithms, and deliver projects with responsibility, while keeping pace with industry trends.

The Data Scientist will leverage critical thinking and data analysis skills to develop Machine Learning models for detecting financial crime risks and synthesizing data requirements.

Lead ML architecture and solution design for NLP, computer vision, and generative AI projects. Define technical designs, review implementations, run POCs, guide developers, and ensure scalable, secure, and ethical deployments using cloud, MLOps, and model deployment pipelines.

Lead design and delivery of ML solutions across NLP, vision, and generative AI. Convert business needs into architecture and designs, guide developers, run POCs, ensure scalability, security, MLOps, and responsible AI best practices, and build/deploy AI agents and LLM-based systems.

Develop and optimize small language models using Hugging Face, TRL, LoRA, QLoRA, and PEFT. Build MLOps pipelines, optimize models through quantization and distillation, deploy them to edge and mobile environments, and monitor production performance. Evaluate model quality with benchmarking frameworks and custom evaluation suites while meeting strict latency and hardware utilization targets.

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

Lead design, operation, and reliability of large-scale AI/HPC clusters. Manage day-to-day operations, incident response, automation, performance tuning, scheduler and storage optimization, and collaborate with researchers and global teams to improve GPU-accelerated infrastructure.

Design, build, and deploy AI agents and machine learning models; integrate ML/NLP into full-stack applications; develop APIs and data pipelines; deploy and manage services on AWS; collaborate with frontend/backend teams and maintain CI/CD, databases, and production monitoring.

Build and deploy production-grade generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, fine-tune and host LLMs, develop scalable FastAPI backends, optimize latency/cost, and containerize services for production.

Design, build, and maintain AI platform and ML pipelines by assembling LLMs and agentic workflows, integrating assistants with systems, prototyping in low-code and code, writing tests and CI/CD, and iterating with stakeholders on domusAI, domiAI, domusDocs and multi-agent frameworks.

Design, build, and scale production Generative AI systems: multi-agent workflows, advanced RAG pipelines, model fine-tuning and quantization, LLM integration and swapping, high-performance FastAPI backends, vector search and retrieval, containerized deployment, and monitoring for latency, cost, and reliability.

Design and implement end-to-end machine learning solutions: analyze use cases, build statistical and ML models, prepare and enrich data, implement scalable PySpark models in Databricks, and collaborate with product and engineering to productionize analytics for personalization and business insights.

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.

Lead design and production of face recognition and biometric CV systems. Build, train, optimize models (PyTorch/TensorFlow/JAX), own end-to-end ML pipelines (Airflow), ensure fairness, deploy low-latency inference on AWS, and mentor engineers.

The role seeks an AI Lead to drive Generative AI and Computer Vision projects, focusing on precise visual transformations and model training while mentoring engineering teams in best practices.

Lead design, development, and production deployment of enterprise-scale ML and GenAI solutions (LLMs, RAG, recommendation systems). Architect scalable inference and training pipelines, mentor MLEs, collaborate with stakeholders, ensure security/compliance, and measure and optimize model performance and business impact.