Top Machine Learning Jobs in Pune

Designs and develops AI and machine learning models, including NLP and computer vision systems, and deploys them into scalable production applications through APIs, microservices, and cloud platforms. Responsibilities include data preparation, model training, testing, debugging, performance optimization, and collaboration with data scientists and software engineers. The role requires strong programming, ML framework, cloud, SQL, and algorithm development skills.

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 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.

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.

Lead delivery of high-performing, scalable software as a hands-on lead for one or two squads. Design and architect distributed, cloud-native microservices, drive prioritization and cross-functional coordination, mentor engineers, ensure quality/performance/security, improve automation and SDLC, and resolve complex cross-service problems.

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.

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.

Lead scientific and strategic AI efforts for Vaccines Medical: define AI vision and roadmap, govern responsible AI standards, prioritize and deliver validated AI-enabled solutions, partner cross-functionally, oversee deployment and adoption, manage budget and vendors, and ensure regulatory, ethical, and scientific compliance across global medical affairs.

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.

Lead technical design and build scalable data pipelines and platforms to support AI Ops, SRE, and analytics. Set standards for quality, security, and observability, mentor engineers, operationalize models, and contribute to roadmap and documentation for the ONE Data Lake.

Sell SailPoint's IGA identity security SaaS to $5B+ enterprises by engaging C-level stakeholders, managing long sales cycles with POCs/RFPs, collaborating with GSIs and channel partners, developing account and opportunity plans, and meeting quota using Challenger methodology.

Lead design and implementation of an Analytic Foundation and scalable big-data services delivered via APIs. Build data pipelines, analytics and data models, responsive UIs, and production ML/data-driven applications. Ensure high-quality, well-tested code, mentor junior engineers, drive process improvements, and collaborate with product and design teams to deliver customer-focused data 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.

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.

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.

Collects, standardizes, cleans, and analyzes data from multiple sources. Supports preliminary insights, machine learning model training, database maintenance, visualizations, reporting, data validation, and documentation under senior team guidance. Collaborates with colleagues to understand data needs and communicates basic findings. Requires foundational Python, SQL, Excel, statistics, data cleaning, and data collection skills.

Designs and supports data platform blueprints, semantic models, ontologies, taxonomies, metadata schemas, and knowledge graphs. Collaborates with integration and data architects to integrate systems and data models. Develops scalable linked-data architectures using RDF, OWL, SKOS, and SHACL, and works with graph databases and query languages. Supports semantic search, data extraction, AI-driven recommendations, and data discovery while communicating complex concepts to technical and non-technical stakeholders.

Develop and deliver pharmaceutical forecasts and analytical solutions using statistical and ML techniques. Collaborate cross-functionally to build assumptions, automate and improve forecasting processes, communicate findings to stakeholders, and maintain documentation and forecasting tools.

Use data analytics and machine learning to extract insights from large transactional datasets, build and maintain data pipelines, develop predictive models, produce MI and visualizations (Power BI/Tableau), support automation and model-driven decision support for payment screening and sanctions, and partner with stakeholders to mitigate financial crime risk.

Lead design, development and maintenance of reports, dashboards and automated report generation. Own data engineering, data quality, lineage and governance; build BI platforms, cloud-native services, and drive DataOps, automation and AI-enabled solutions while engaging stakeholders and leading teams.

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.

Define and govern Sensata’s enterprise AI architecture while remaining hands-on in designing, prototyping, and guiding production AI systems. Develop reference architectures, data and model lifecycle standards, GenAI guardrails, RAG and agent patterns, integration approaches, evaluation and observability practices, and reusable implementation assets. Partner with engineering, data, security, legal, compliance, and business teams; evaluate vendors and build-versus-buy options; prioritize use cases; and provide technical leadership for scalable, responsible AI adoption.

Leads the design, development, and delivery of scalable enterprise software, integrations, APIs, microservices, and cloud-native platforms. Builds automation and intelligent workflow solutions using Python, AI, and machine learning. Oversees infrastructure, deployment pipelines, monitoring, security, testing, operational support, and third-party connectivity. Collaborates with product, engineering, and business stakeholders, mentors teams, manages risk and controls, and drives engineering excellence across the organization.