Top Machine Learning Jobs in India

Leads the design and delivery of production-grade AI and machine learning solutions, including predictive maintenance for MRI and CT equipment. Oversees end-to-end ML workflows, model monitoring, cloud infrastructure, streaming data integration, architecture governance, and responsible AI practices. Mentors data scientists and ML engineers while partnering with engineering, product, data, and UX teams to deliver scalable, reliable solutions.

Develops and deploys production AI and machine learning systems, especially NLP and retrieval-augmented generation solutions. Manages cloud-based model training, deployment, monitoring, and enterprise-scale applications using Azure, AWS, or GCP. Applies deep learning, statistics, optimization, and software engineering practices, while supporting MLOps, LLMOps, CI/CD, and infrastructure automation. Serves as a technical resource and communicates complex analytical concepts to stakeholders.

Analyze complex business and customer-resolution problems, translate data requirements into product features, identify AI and GenAI opportunities, interpret analytical outputs, and communicate actionable recommendations. The role partners with technology, product, and business teams to improve complaint resolution workflows and platform adoption. Candidates need a quantitative bachelor’s degree, at least two years of relevant experience, and preferably Python, R, SQL, relational database, statistical modeling, and financial services experience.

Analyze complex business and customer-resolution problems, translate data requirements into product features, and identify opportunities to apply AI and generative AI. Partner with technology, product, and business teams to improve workflows, interpret analytical outputs, communicate insights to leadership, and support adoption of the Customer Navigator platform.

Lead technical vision and architecture for large-scale, low-latency data systems; contribute hands-on to core code and production support; drive cross-team alignment; mentor engineers; refine data pipelines and CI/CD; apply AI tools to improve workflows; collaborate with Ads and Bing to integrate shared infrastructure and deliver reliable, scalable platforms.

Lead end-to-end AI Economy Trust & Safety detection systems, including identity resolution, account-risk modeling, abuse detection, and clustering. Develop and evaluate supervised, anomaly detection, graph, sequence, and large language models. Establish frameworks for calibration, drift, explainability, false-positive impact, and adversarial robustness. Partner with product, engineering, privacy, legal, and external teams to deploy effective controls and mentor technical staff.

Leads and develops scalable data pipelines, APIs, microservices, and ETL/ELT systems for regulatory and health agency datasets. Ensures data quality through automated testing, monitoring, and validation; integrates pipelines with NLP, machine learning, and search services; and supports production incident resolution. Mentors junior and mid-level engineers, establishes technical standards, conducts code reviews, and translates architecture and product requirements into actionable engineering work.

Responsible for selling 75F building automation and energy efficiency solutions to enterprise and channel customers. Duties include networking with clients, builders, contractors, architects, and consultants; delivering presentations and product demonstrations; preparing sales reports and forecasts; matching solutions to customer requirements; expanding strategic accounts; and supporting site surveys, audits, and project proposals. Requires extensive HVAC, building controls, and energy management sales experience across channel and enterprise models.

Develops and implements cognitive computing solutions using AI and machine learning, including complex algorithms and NLP. Responsibilities include developing AI models, training machine learning models, collaborating with engineers, and optimizing AI algorithms.

Develop and implement cognitive computing solutions using artificial intelligence and machine learning. Build, train, optimize, and deploy AI models and complex algorithms, including natural language processing systems. Collaborate with engineering teams to deliver machine learning solutions. The role requires a bachelor’s or master’s degree in engineering and 2–5 years of experience.

Develop and maintain full-stack applications using React and Python. Collaborate effectively with the team, communicate clearly, take ownership of responsibilities, and work independently as a self-starter. The role requires working from Gurgaon three days per week and may involve technologies including GCP, machine learning, web services, Git, Power BI, Node.js, Power Apps, and REST services.

Develop and optimize machine learning and NLP models for contact center use cases, including call deflection, intelligent routing, agent assistance, and quality monitoring. Perform feature engineering, model evaluation, documentation, deployment monitoring, and drift detection. Collaborate with operations and technology teams to integrate model outputs and assess impact on operational KPIs.

Develops and deploys machine learning and NLP models, including customer segmentation, lifetime value, and marketing optimization solutions. Responsibilities include data preparation, feature engineering, model training, evaluation, tuning, quality checks, analytics insights, and client deliverables. Translates business requirements into analytical problems, analyzes complex datasets, and contributes to solution methodology and client engagement.

Leads insurance analytics and actuarial regulatory projects, including data modeling, predictive modeling, reporting, visualization, and automation. Manages client and team relationships, project deliverables, stakeholder meetings, issue resolution, and process improvements. Acts as a functional and domain expert while identifying growth opportunities and ensuring industry best practices. Requires strong expertise in SQL, SAS, Python, statistical techniques, and insurance analytics.

Provides analytics consulting support to Sports and Media clients by extracting insights from data, developing SQL and Python solutions, applying machine learning, creating visualizations and reports, and leading small analytics teams. Collaborates with clients to understand business objectives, communicate findings, improve analytics processes, and support data-driven decisions.

Develop and refine generative AI and large language models, integrate AI solutions into business processes, collaborate with cross-functional teams, conduct machine learning research, and communicate technical findings through presentations, white papers, blogs, and strategic recommendations.

Develop and maintain Tableau dashboards, reports, and data models; write complex SQL queries; use Hive and Python for data transformation and automation; conduct ad hoc analysis; validate data accuracy and dashboard performance; and collaborate with stakeholders, data engineers, and data scientists. Payments and fraud experience is preferred, while machine learning knowledge is advantageous.

Lead development and deployment of AI solutions focused on Generative AI, deep learning, NLP, computer vision, and AI agents. Design neural network architectures, train and test models, cleanse and impute data, integrate solutions into real-time applications, and deploy them on AWS, Azure, and on-premise environments. Collaborate with cross-functional teams, troubleshoot implementations, report project progress, and ensure compliance with AI best practices.

Leads client-facing banking analytics engagements involving credit risk, fraud detection, or marketing analytics. Builds and validates statistical and machine learning models, translates findings into business recommendations, ensures regulatory compliance, presents insights to clients, and mentors junior team members. Responsibilities include data wrangling, feature engineering, model validation, and analytics project leadership.

Perform end-to-end system verification of Ericsson 5G RAN solutions, including capacity, performance, scalability, mobility, and high-load traffic testing. Analyze logs, traces, KPIs, and protocol-layer behavior; troubleshoot PHY/MAC/RLC/PDCP/IP issues and identify root causes. Support feature integration, test automation, verification frameworks, and CI/CD improvements. Collaborate with development, integration, CI, and product teams while applying modern and AI-assisted verification methods.

Leads Sulzer’s Advanced Analytics and AI function, converting business opportunities into governed, production-grade statistical, machine-learning, GenAI, and agentic-AI solutions. Owns the service portfolio, delivery standards, lifecycle, adoption, monitoring, partner model, and production value metrics. Builds and coaches internal teams, collaborates with senior stakeholders and enterprise architecture, and ensures data quality, security, privacy, responsible AI, and operational safety requirements are met.

Lead the design, development, deployment, and optimization of production-grade Generative AI systems. Build RAG pipelines, fine-tune foundation models, develop AI agents, establish evaluation and observability frameworks, and integrate multimodal AI capabilities. Collaborate with product, engineering, data, and business stakeholders to deliver scalable, reliable, secure, and responsible AI solutions.

Collect, clean, model, and analyze marketing data to generate customer, campaign, and market insights. Lead predictive analytics, machine learning, A/B testing, visualization, and reporting efforts to optimize targeting and communications. Present actionable recommendations to stakeholders and partner with Marketing, Data Product, Coverage, and MarTech engineering teams. The role also involves influencing decisions, managing risk and controls, and potentially leading and developing team members.

Develop and maintain automated quality testing for enterprise data products, pipelines, transformations, APIs, and platform capabilities. Validate data correctness, completeness, freshness, consistency, and lineage; build reconciliation and monitoring frameworks; design regression suites; and investigate production data-quality issues. The role also evaluates AI-powered analytics capabilities by validating user intents, creating evaluation datasets, measuring response accuracy, and identifying hallucinations and edge cases.

Owns strategy, roadmap, and end-to-end delivery for automotive Parts product capabilities, including AI-powered workflows, OEM and third-party integrations, and REST API extensibility. Partners with engineering, ML/AI, design, data, GTM, and customer teams to improve dealership productivity, adoption, workflow efficiency, integration reliability, and customer outcomes.