Top Machine Learning Jobs in Pune

Builds and maintains scalable data pipelines, data architecture, ETL infrastructure, and analytics tools. The role assembles complex datasets, improves data delivery and infrastructure scalability, supports cross-functional stakeholders, and enables actionable business insights. Required expertise includes SAS, Python, Java, MATLAB, SQL and NoSQL databases, Hadoop-based data warehousing, MapReduce, Hive, and Presto. The position requires 5–8 years of related experience and a bachelor’s degree or equivalent experience.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Seeking senior software engineers with proven expertise in at least one technical area, including AI/ML, LLMs, data, full-stack or backend development, automation, cloud infrastructure, and DevOps. Responsibilities include solving complex problems independently, collaborating across technologies, and delivering production-quality projects in a startup environment. Engagements may be project-based or ongoing.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

Deliver and support a real-time sanctions platform transformation using FircoSoft Continuity. Responsibilities include system integration, data architecture, MQ-based messaging, deployment and redundancy design, vendor coordination, data governance, and scalable solution delivery. The role also requires mentoring stakeholders, translating complex technical concepts, and ensuring auditable, testable implementations delivered on time and within budget.

Analyze product, customer, and business data to identify trends, risks, and opportunities. Build dashboards, metrics, reports, analytics solutions, and predictive models while supporting data pipelines and AI-powered automation. Partner with Product Managers, Engineers, customers, and business stakeholders to define requirements, improve workflows, validate product ideas, and measure outcomes. Own initiatives from problem identification through delivery while developing expertise in product management, data engineering, AI, and enterprise financial technology.

The AI Data Scientist will analyze data using machine learning to provide insights for decision-making and improve operational efficiency, as well as lead and mentor teams in AI initiatives.

Perform advanced analytics on large datasets, design and build automated data pipelines, investigate data quality and lineage, and create interactive dashboards. Translate insights into actionable campaign and process recommendations, partner with stakeholders and support campaign delivery and BAU operations.

Lead the architecture and development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and AI lifecycle capabilities. Drive security, governance, observability, reliability, performance, and cost optimization while translating R&D concepts into production systems. Provide hands-on technical leadership, architecture reviews, mentorship, and cross-functional collaboration.

Lead the architecture and hands-on development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and ML lifecycle capabilities. Establish standards for security, governance, testing, CI/CD, observability, reliability, and cost optimization while providing technical leadership, mentoring, architecture reviews, and cross-functional collaboration.

Develops and maintains business intelligence reports, dashboards, data models, and visualizations using Power BI, DAX, SQL, and enterprise data platforms. Extracts, transforms, validates, and analyzes data to generate actionable insights and support decision-making. Collaborates with stakeholders to gather reporting requirements, communicates findings, supports data governance, and improves reporting processes through automation and emerging technologies.

The Customer Success Business Partner at Lyric drives customer adoption and retention for strategic accounts, leveraging expertise in AI and supply chain operations.

Build and productionize machine learning, LLM, and AI solutions with Data Science teams. Design industrialized data-processing pipelines, implement MLOps and LLMOps frameworks, manage model lifecycles, and develop production Python, ML, and cloud solutions using Azure, GCP, Databricks, Spark, and PySpark. Gather requirements, estimate work, document solutions, present results, and support technical best practices.

Lead statistical activities for Phase I–IV clinical trials from protocol design through results reporting. Provide biostatistical expertise for clinical development plans, protocols, analyses, safety reporting, regulatory inquiries, and publications. Collaborate with cross-functional teams, oversee internal programmers and CRO vendors, use SAS and R for statistical analyses and quality control, apply machine learning methods, and mentor junior statisticians.

Serve as a technical expert throughout enterprise sales cycles, partnering with account executives and customers to identify requirements, demonstrate SingleStore solutions, develop and deploy proofs of concept, lead technical evaluations, tune queries, conduct load testing, and support account expansion and channel partnerships. The role requires expertise in distributed databases, data platforms, AI and ML, cloud and containerized environments, programming, and enterprise technology sales.

Own the strategy, roadmap, and lifecycle of provider data management products, including directories, registries, master data, data quality, enrichment, and APIs. Translate customer and business needs into product requirements, guide discovery and delivery, define metrics, and collaborate with engineering, data science, analytics, UX, AI, and healthcare stakeholders. Use SQL, analytics, experimentation, and emerging technologies to improve provider data accuracy, operational efficiency, and customer outcomes.

Designs and oversees enterprise software architecture, development, testing, implementation, and technical documentation. Partners with stakeholders and Product teams to define requirements, evaluate technologies, create scalable solutions, review code, support application integrations, and guide development teams. The role also coordinates technology projects, training, system improvements, security, quality, and extensibility while providing technical support for case and client efforts.

Develop and ship Yahoo Mail mobile features for Android, owning projects from technical design through deployment, monitoring, and improvement. Build scalable solutions using Kotlin, Java, Android frameworks, Jetpack Compose, and React Native; integrate APIs; address performance, reliability, and usability challenges; conduct code reviews; collaborate cross-functionally; mentor teammates; and use AI development tools to improve engineering productivity while maintaining quality and security.

Leads enterprise data architecture strategy and develops scalable data platforms, pipelines, warehouses, lakes, governance frameworks, and analytics solutions. Designs data models, integrations, migrations, and reporting architectures across AWS, Azure, Databricks, Snowflake, and BI platforms. Partners with AI/ML teams, business stakeholders, and senior leadership to modernize data capabilities, optimize performance, establish standards, and deliver complex analytical solutions. Mentors data engineers and architects while advancing data quality, security, compliance, and architectural governance.

Lead design and implementation of ML tools for scenario classification, anomaly detection, sensor data quality, behavior analytics, and automated reporting. Build scalable pipelines and automated data curation for camera, radar, and LiDAR; create SIL/HIL replay interfaces, ground-truthing and annotation tools, and offline benchmarking. Integrate ML modules into simulation, HIL/SIL, and CI/CD ecosystems, implement reproducible workflows (Docker, CI/CD, artifact versioning, automated tests), monitor model/data quality and mentor junior engineers while collaborating with verification and perception teams.

Own Tala’s product strategy and roadmap for fraud prevention, authentication, identity, and risk capabilities. Partner with Engineering, Data Science, Analytics, and business teams to build data-driven fraud products, define decisioning metrics and thresholds, manage fraud incidents, improve customer security, and minimize friction. Lead cross-functional execution from discovery through launch and iteration while managing external technology partners.

Leads engineering strategy, architecture, delivery, and operations for NVIDIA’s AI-powered employee productivity platform. Manages and develops engineering teams building web, mobile, and agentic experiences at global scale. Oversees technical roadmaps, distributed systems, enterprise integrations, reliability, security, privacy, observability, and responsible AI practices while partnering with Product, Design, HR, Security, SRE, and business stakeholders.

PhD intern will research and prototype AI techniques for Azure SQL Database, analyze algorithms on large datasets, build scalable ML systems, collaborate with researchers and product teams, and contribute to technology transfer and publications.

Implement machine learning models in production and design enterprise GenAI, LLM, and AI automation solutions. Build and manage industrialized processing pipelines, establish ML lifecycle and MLOps/LLMOps best practices, integrate Microsoft AI and Power Platform services, and support data science teams. The role also involves gathering requirements, estimating work, presenting technical solutions, troubleshooting AI agents, and creating documentation.

Implement machine learning models in production and design enterprise GenAI solutions, AI agents, and scalable LLM/ML automation. Build and manage industrialized processing pipelines, MLOps and LLMOps frameworks, and model lifecycle best practices. Integrate Microsoft Copilot Studio and Azure AI services with Power Platform tools, implement RAG and knowledge grounding, support multilingual retrieval, and troubleshoot agents. Gather requirements, estimate work, present technical solutions, and create documentation.