Top Machine Learning Jobs in India

Lead Tier 2/3 diagnostics and resolution for cloud-native 4G/5G core outages, drive root-cause analyses and chronic-incident fixes, implement AI-driven monitoring, and provide technical guidance and training to the NOC to reduce MTTR and improve network stability.

Lead hands-on experimentation and prototype development for Generative and Agentic AI, focusing on LLMs, RAG, vector retrieval, and production-ready solution blueprints. Drive POCs to production by creating reusable prompts, architectures, evaluation frameworks, and implementation artifacts while partnering with engineering and product teams to ensure scalability, cost optimization, and responsible AI practices.

Lead data engineering efforts to ingest, unify, process, and analyze large, high-dimensional datasets using cloud and big-data platforms. Partner with product, data science, and platform teams to build reusable, production-ready data solutions, prototypes, and analytics that drive cybersecurity and fraud-related products and insights.

Lead and drive network improvement projects to reduce work orders/alarms, enhance customer experience and network performance. Analyze alarms, tickets and KPI trends, recommend mitigations, maintain risk and knowledge registers, and collaborate with data analytics to build predictive and prescriptive models for proactive problem prevention.

Develop and maintain Java/Python Spring Boot microservices, work with streaming (Kafka/KSQL), databases (Oracle/MySQL/SQL/PLSQL), UI frameworks (React/Angular), cloud/Kubernetes, and build or integrate ML/GenAI solutions to improve health technology platforms.

Design and deploy production-grade ML and GenAI solutions (LLMs, RAG, AI Agents). Align models to business goals, build scalable ML systems, validate models with evaluation frameworks, mitigate bias, integrate GenAI with enterprise platforms, and collaborate cross-functionally to drive data-driven decisions.

Join an agile Consumer & Community Banking engineering team to design, develop, and troubleshoot secure, stable, scalable software. Contribute to SDLC, write high-quality code, leverage automation and CI/CD, work with data engineering/ETL/SQL and data warehouses, analyze datasets, and grow technical skills in cloud/AI/ML while supporting application resiliency and security.

Lead architecture for conversational AI, advanced search, and scalable self-service platforms. Produce solution designs, guide engineering teams, integrate AI capabilities securely, and establish reusable, auditable AI-enabled SDLC patterns.

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 and implement customer experience data solutions using Qualtrics and analytics platforms. Develop taxonomies, dashboards, ETL processes, and automation; ensure data quality, controls compliance, and stakeholder enablement. Support R&D on emerging technologies and deliver data-driven insights to improve customer journeys.

Investigate and manage quality deviations (QDR), perform root-cause analysis (5-Why, Fishbone, 8D, FMEA), execute containment and disposition, monitor equipment and process performance, coordinate CAPA with stakeholders, drive yield/quality improvements, and use Excel/Power BI and AI tools to analyze trends and automate QDR reporting.

Lead a team producing labeled datasets for ML and generative AI in financial services. Own end-to-end delivery, define annotation guidelines and taxonomies, measure and validate label quality, implement human-in-the-loop and prompt-based workflows, and partner with data science and engineering to ensure reliable ground truth across text, documents, chat, email, and audio.

Lead design and adoption of cloud-native architecture and AI-enabled engineering across Asset & Wealth Management. Advise on technology selection, create scalable reusable frameworks, develop and review secure production code, set multi-year roadmaps, and establish governance and guardrails for agentic AI, automation, and responsible AI use at scale.

Designs, develops, tests, and maintains secure, scalable software across the SDLC. Produces architecture artifacts, debugs complex systems, leverages AI-assisted development tools, analyzes large data sets to improve code and architecture, and contributes to engineering communities while promoting secure and resilient application practices.

Lead and own the Data Science and AI function, driving analytics strategy, stakeholder engagement, portfolio outcomes, team leadership, technical design, and compliance with data policies to deliver measurable business value.

Lead a 50-70 person engineering organization to modernize Atlassian's Commerce Platform into a unified, multi-modal billing and monetization engine. Own architecture, reliability, and financial integrity of billing and finance integrations (including Stripe). Drive AI-native features, enterprise-grade commerce capabilities, and zero-error operational excellence while influencing C-level stakeholders on roadmap and execution.

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.

Lead plant digital transformation by running pilot projects, evaluating disruptive technologies, partnering with startups, implementing MES/IIoT, and owning end-to-end product management and adoption to improve efficiency, quality, and sustainability.

The Director of Data Platform Engineering will oversee the data infrastructure, manage relational databases, lead AI integration, and ensure data governance while fostering a collaborative team environment.

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 end-to-end design and delivery of AI/ML and generative-AI solutions for sales and distribution modernization. Build scalable platforms, API-driven integrations, RAG and agentic pipelines, and production MLOps. Ensure performance, security, Responsible AI governance, mentor distributed teams, and collaborate with product, sales, operations, and security to operationalize automation and sales-optimization workflows.

Perform statistical analyses and bias testing on traditional and ML/AI lending models, build mitigation algorithms, document results, support governance and regulatory exams, and present findings to stakeholders.

Support global country risk data collection, aggregation and variance analysis. Represent Middle Office in reviews, improve reporting processes, perform data quality checks, partner with risk, finance and tech teams, and ensure timely SLA-driven reporting and controls.

Design, validate, and monitor card fraud strategies using data analysis and analytics/AI. Partner with product, technology, and operations to develop, test and implement detection and treatment capabilities, balancing fraud mitigation with revenue, costs, and customer impact.

Perform market-microstructure and algorithmic execution research: develop mathematical models for single-stock and portfolio execution, order placement and routing, build quantitative tools for order-flow analysis, optimize execution performance, and publish white papers to translate research into trading improvements.