Top Machine Learning Jobs in India

Develop and lead Generative AI, LLM, and machine learning solutions for enterprise clients. Responsibilities include translating business needs into data problems, building and evaluating statistical models, creating cloud-based POCs and POVs, presenting solutions to customers, deploying models, and applying MLOps, data engineering, DevOps, and governance practices. The role requires strong expertise in NLP, LLMs, RAG, fine-tuning, AI architecture, optimization, and multiple cloud platforms.

Develops reactor, kinetic, process, and analytics models for refinery and petrochemical technologies. Responsibilities include yield estimation, plant data analysis, catalyst lifecycle prediction, process optimization, model validation, KPI development, and technical collaboration with R&D, engineering, software, and customer teams. Requires expertise in reaction engineering, process simulation, statistical analysis, numerical methods, and programming.

Develops reactor, kinetic, process, and analytics models for refinery and petrochemical technologies. Responsibilities include yield estimation, plant data analysis, catalyst life prediction, forecasting, KPI development, model validation, process optimization, and technical collaboration with R&D, engineering, software, technical services, and customers. Requires expertise in reaction engineering, kinetics, process simulation, statistical analysis, numerical methods, and programming.

Lead KONE’s global People Analytics domain by defining its strategy, operating model, technology roadmap, and governance. Own analytics solutions in partnership with HR, IT, and business stakeholders; represent People & Communications in data governance forums; and support training, data literacy, and standardized people-data practices worldwide. The role requires leadership across business and technical teams, knowledge of HR analytics technologies such as Workday and Power BI, and understanding of AI, machine learning, automation, cloud infrastructure, and data architecture.

Leads investment technology strategy and global engineering teams while remaining hands-on in Java and Python. Oversees AWS cloud platforms, full-stack development, data enablement, advanced analytics, and production Generative AI solutions for investment workflows. Translates investment-domain needs into scalable decision systems, reporting, and portfolio analytics. The role manages strategic initiatives, stakeholder alignment, governance, compliance, delivery, recruitment, mentoring, and organizational change across a matrixed environment.

Lead strategic and operational initiatives for executive stakeholders, including operating model optimization, margin improvement, budgeting, and digital transformation. Analyze current and future-state operations, identify growth bottlenecks, develop solutions using data, customer interviews, and market research, and govern projects through completion. Build data models and analytics to present insights and trends to leaders while serving as a key contact for Consulting department users and executives.

Analyze HVAC energy data to drive client energy savings and efficiency targets. Responsibilities include energy audits, monitoring client sites, developing dashboards and reports, identifying efficiency improvements, building retention models, supporting installations and commissioning, resolving technical issues, and providing actionable analytical recommendations. The role requires strong energy-domain knowledge, large-dataset analysis, statistical and visualization methods, client communication, and working knowledge of BMS and energy calculations.

Lead SAP FTM (Finance-To-Manage) configuration and delivery for S/4HANA: design global finance templates, configure FICO/FTM modules (GL, AP/AR, fixed assets, treasury, RAR, FCSM), lead WRICEF design and testing, ensure integrations (PTP, OTC, MM, SD, EWM, IBP), support IDOC/EDI, drive issue resolution and automation, and engage stakeholders for requirements, training, and compliance.

Designs and delivers enterprise AI and Agentforce architectures from strategy through production. Establishes architecture standards, governance, scalability, security, and responsible AI practices; coordinates engineering, data, product, and delivery teams; resolves technical dependencies; builds agents, automations, integrations, and prototypes; monitors architecture health; provides technical escalation support; and mentors architects and engineers.

Design, build, and deploy enterprise-grade LLM and RAG-based AI solutions, including agentic AI agents, vector search pipelines, prompt engineering, observability, cloud deployment (AWS/Azure), and integration into microservices and APIs. Collaborate with stakeholders to translate business needs into scalable, secure, production-ready AI systems.

Leads consultative solution discovery and technical presales for AspenTech APM and related software across Oil & Gas, Chemicals, Power, Mining, and Food & Beverage industries in APJ. Develops customer-specific solutions, value propositions, proposals, scopes of work, sales campaigns, and implementation roadmaps. Partners with sales, consulting, and customers to identify business challenges, demonstrate software capabilities, and drive pipeline growth. Requires strong industry, technical, analytical, communication, and consultative skills, with 50–60% regional travel.

Leads the design, development, deployment, and maintenance of AI/ML-enabled healthcare technology systems. Responsibilities include developing scalable algorithms and backend components, troubleshooting and testing models, protecting sensitive healthcare data, prioritizing AI-driven features, coordinating with stakeholders, setting project standards, and advising on AI/ML strategy. The role also requires leadership of AI/ML projects, technical communication, risk management, and continuous innovation.

Provide mid-level technical support by troubleshooting and resolving customer issues, managing support tickets to meet SLAs, collaborating with cross-functional teams, improving support processes, and delivering training to junior staff. Support multiple Zebra products and use AI-driven tools to diagnose and resolve problems.

Provide intermediate-level technical support for multiple Zebra products: troubleshoot and resolve moderate technical issues, manage support tickets within SLAs, collaborate cross-functionally, contribute to process improvements, and train entry-level team members.

Owns product features from discovery through rollout, including writing user stories, leading Agile sprint ceremonies, coordinating cross-functional teams, managing QA, and supporting product roadmaps. Monitors product utilization and analyzes trends to optimize the platform. Partners with engineering, marketing, clients, operations, legal, and other stakeholders to align product execution with customer needs and business objectives.

Lead the Data Analytics function, analyze large datasets, design analytics frameworks, build Tableau/Power BI dashboards, implement AI/ML predictive solutions, ensure data governance, present insights to senior leadership, and mentor data analysts.

Senior ServiceNow IRM engineer responsible for designing, building, testing, and deploying ServiceNow IRM solutions. Duties include configuration, scripting, integrations, code reviews, documentation, unit/integration/UAT testing, Agile participation, and preparing production configurations with release/train engineers.

Develop and deploy reporting solutions using Python and generative AI. Work hands-on with LLMs and frameworks (OpenAI, Hugging Face, LangChain, LlamaIndex) and apply machine learning and deep learning concepts to build models and integrations.

Develop and optimize machine-learning models and trading signals for market-neutral strategies. Parse large datasets, implement academic research, backtest hypotheses, and collaborate with researchers to build scalable systematic trading strategies across equities, futures, commodities, and event-driven research.

Partner with semiconductor customers to drive RTL-to-GDSII implementation on advanced nodes, develop methodologies, debug tool issues, deploy ML productivity solutions, influence product direction, collaborate with sales and mentor AE teams.

Lead-capable engineer who designs, ships, and operates production AI/agent systems and internal AI platform services. Own multi-person workstreams, set technical direction, ensure evaluation, observability, safety, and cost controls, partner with business owners to translate ambiguous problems into measurable solutions, and mentor engineers to raise team engineering standards.

Join the AI Engineering team to implement features for agents, copilots, and internal AI platform services. Write clean, tested code, integrate tools and data sources, build evaluation harnesses, and apply modern AI engineering practices (prompting, RAG, tool use). Collaborate with stakeholders, participate in code reviews, design discussions, and on-call rotation while growing ownership and fluency in agentic systems.

Design, build, and ship agentic systems, copilots, and platform components end-to-end. Implement orchestration, retrieval, memory, tool use, evaluation, observability, and on-call readiness. Partner with stakeholders, mentor junior engineers, and improve internal AI platform tooling and evaluations for production reliability.

Own the vision, strategy, and roadmap for AI-powered network observability products. Collaborate with engineering, design, data science, and GTM teams to integrate ML models for anomaly detection, root cause analysis, and predictive analytics. Drive product lifecycle from concept to launch, define KPIs, engage customers for feedback, and ensure successful SaaS/cloud-based deliveries.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.