Top Machine Learning Jobs in Pune

Lead large-scale digital transformation programs for supply chain and manufacturing clients using AI/ML, Industry 4.0/5.0, IoT/IIoT, cloud, and automation. Advise C-level stakeholders, architect digital solutions (digital twins, predictive analytics, MES/SCADA integrations), manage multi-geography teams, drive change management, and support pre-sales and practice growth.

Build, deploy, and optimize ML/AI models using large structured and unstructured datasets. Design and automate ETL/data pipelines, improve data quality, and develop AI-enabled product features. Collaborate with product and engineering teams to translate business problems into scalable, production-ready intelligent solutions.

Lead the development of generative AI systems, overseeing multi-agent AI architectures, machine learning pipelines, and implementing responsible AI practices. Drive innovation in AI applications using various frameworks and databases while managing cross-functional teams.

Develop statistically effective algorithms and apply machine learning, deep learning, and generative AI techniques to high-dimensional problems. Use hypothesis testing, data mining, and big-data tools to identify trends, build predictive models and recommender systems, collaborate with clients and stakeholders, and evaluate emerging datasets and technologies for scalable analytical solutions.

The Engineering Lead will design and implement technical features, work on ML pipelines, and mentor engineers while ensuring high-quality deliverables. This role requires collaborating with teams and managing the ML lifecycle for business value.

The Senior Technical Program Manager will drive execution across AI, data, and engineering initiatives, ensuring alignment, delivery rigor, and risk management.

Design and implement technical features, collaborate with clients, write production-ready code, ensure quality deliverables, mentor team members, and implement ML models on large scale.

Design and build production-grade AI applications embedding ML/LLM capabilities. Integrate trained models into APIs, services, UIs, and workflows. Operationalize models (versioning, deployment, monitoring), implement guardrails, ensure security/privacy/compliance, apply bias mitigation and explainability, and mentor team members on AI engineering best practices.

Lead GBSC data engineering and analytics initiatives: design data/ETL infrastructure, implement automation, enable large-scale structured and unstructured data analysis, manage team delivery, and communicate insights to influence business decisions.

Lead and grow a data science team to deliver predictive and ML-driven insights. Partner with business and engineering to build production-ready models, enforce model governance, ensure explainability, and measure business impact while driving continuous improvement.

Build data-driven solutions and BI using Python, Power BI, and SQL. Implement DevOps pipelines with GitHub Actions/Azure DevOps, deploy on Azure/AWS, integrate APIs, apply unit testing and automation, and collaborate in Agile/Scrum with global teams. Support solution design, effort estimates, and use ML/DL/LLMs for intelligent features.

Design, build, and maintain scalable data pipelines and cloud data platforms to support AI/ML workloads. Deploy, monitor, and operationalize machine learning models, implement CI/CD and MLOps, ensure data quality, scalability, and governance while collaborating with data scientists, engineers, and stakeholders.

Design and implement scalable backend services, lead full software lifecycle, write design docs, mentor engineers, collaborate cross-functionally, improve performance and quality, and integrate AI tools to boost development productivity.

Perform static and dynamic malware analysis, develop automation and detection signatures, conduct IOC/reputation analysis, validate AI/ML model outputs, and produce threat research and tooling to protect and scale Zscaler's platform.

Develop and maintain DirectX graphics drivers for NVIDIA GPUs, design new OS and GPU features, improve graphics and AI-based rendering, build AI-driven engineering automation and agent-based tooling, collaborate across teams to drive software objectives and quality improvements for gaming and graphics workloads.

Perform advanced analysis of large datasets, design and build data pipelines, investigate data quality and lineage, create interactive dashboards and reports, translate insights (including ML/AI) into business recommendations, and lead or guide teams while ensuring controls and governance.

Consult with CPG/FMCG clients on Revenue Growth Management analytics programs, focusing on pricing strategies, promotion analysis, and optimizing revenue growth while leading engagements across diverse global markets.

Lead advanced statistical analyses and machine learning projects, partner with cross-functional teams to design data-driven solutions, manage large datasets with governance best practices, build predictive models to forecast trends, and communicate insights to stakeholders.

Design and own end-to-end solution architectures, translate requirements into technical designs, evaluate technologies, define integrations, guide development and DevOps teams, conduct reviews and POCs, support pre-sales, and ensure compliance with ISO standards.

Drive sales of CCTech digital transformation solutions to AEC, construction, and infrastructure clients. Prospect and manage pipeline, conduct discovery and solution positioning, support pre-sales workshops and proposals, collaborate with engineering teams, and develop strategic accounts focused on BIM, digital twin, automation, analytics, and enterprise integrations.

Lead data analysis and engineering to enable data-driven decisions: investigate data quality and lineage, build automated pipelines, apply advanced analytics and ML, design interactive dashboards and reports, translate insights into business recommendations, and support governance and controls while coaching or leading team members.

The role focuses on developing analytical data science products, implementing algorithms for insights, and expertise in deep learning frameworks.

Advise clients on developing and implementing data governance strategies. Design policies, frameworks, and practices for data quality, privacy, metadata management, and security. Deliver governance projects, build capability with frameworks and accelerators, and promote governance awareness across stakeholders and committees.

The Solution Architect will design, develop and implement cloud-native platforms for fraud detection, balancing technology risks and business needs, while collaborating with stakeholders and ensuring compliance with governance processes.

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.