Top Machine Learning Jobs in Delhi

Build and scale machine learning systems for search, including ranking models, feature engineering, model deployment, monitoring, experimentation, and online inference. Design fault-tolerant distributed infrastructure, optimize search latency and throughput, and develop reliable ML services from proof of concept through production. Collaborate across engineering and product teams, communicate technical decisions, review code, document systems, and mentor junior engineers.

Develops and integrates radiating components and passive radiation-pattern structures for multiband base-station antennas. Responsibilities include 3D and 2D electromagnetic simulation, prototype RF measurements, performance optimization, troubleshooting, technical documentation, and supporting the product lifecycle through mass production. The role collaborates with international cross-functional teams and requires relocation to Rosenheim, Germany.

Lead presales Solution Architect for UiPath Intelligent Automation. Run discovery workshops, design scalable enterprise automation, deliver demos/PoCs, produce technical proposals, define governance and integrations, and mentor teams while driving CoE and adoption.

Develop proofs of concept and production-oriented data science and AI solutions for government and organizational clients. Responsibilities include identifying use cases, preparing and assessing data, statistical modeling, forecasting, machine learning, LLM prototyping, evaluation, documentation, stakeholder communication, and client delivery. The role will establish data science capabilities for ICF’s India team and contribute to proposals, reusable processes, and project implementation.

Partners with enterprise customers to understand requirements, design AI solution architectures, and guide production deployments. Builds integrations, prototypes, reusable tools, and technical assets while troubleshooting complex issues. Leads technical workshops, collaborates with Product and Engineering teams, and shares customer feedback to improve the platform. The role combines software engineering, customer engagement, AI deployment, and scalable solution development.

Design and deploy scalable AI solutions for enterprise customers. Build integrations, prototypes, reusable tools, and production-grade applications; lead technical workshops; troubleshoot complex issues; document best practices; and collaborate with Product and Engineering teams to improve the platform based on customer feedback.

Performs satellite image classification and time series analysis using Sentinel and PlanetScope data. Develops and optimizes machine learning pipelines, Google Earth Engine applications, geospatial models, predictive models, decision-support tools, and dashboards. Coordinates ground-truth data collection with field staff, supports stakeholder engagement, and prepares reports, research papers, and communications.

Evaluates and manages machine learning research manuscripts for Nature Communications, overseeing peer review, editorial decisions, author and reviewer relationships, and publishing workflows. The editor will help shape journal strategy, attract high-quality submissions, commission reviews, represent the journal at conferences, monitor scientific trends, and contribute to scientific content and editorial policy development.

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.

Leads research conceptualization, methodological and sampling design, instrument development, ethics governance, fieldwork, data quality, quantitative and qualitative analysis, and report writing. The role requires managing research projects, applying machine learning and statistical methods, using analytical software, communicating findings, and translating evidence for policymakers and other stakeholders. Prior experience in the skill ecosystem and strong analytical, reporting, presentation, and data-management capabilities are required.

Leads strategy and execution for pricing, driver matching, and incentive products supporting delivery operations across international markets. Owns product vision, roadmaps, discovery, experimentation, analytics, launch execution, and value realization. Partners with Engineering, Design, Analytics, and business stakeholders to optimize driver experience, delivery performance, and cost per order. Influences technical direction, evaluates AI opportunities, mentors product managers, and addresses local regulatory and market requirements in Canada and Latin America.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

Conduct experimental research in machine intelligence, develop and refine machine learning algorithms and models, apply research findings to existing AI systems, collaborate with engineers and researchers, publish results, and stay current with advances in machine learning.

Develop statistical and machine learning models to optimize Akamai’s global mapping service. Analyze massive telemetry streams, build predictive observability tools and automated data pipelines, define reliability metrics, investigate outages, and guide architecture and technology investment decisions. Collaborate with product engineers and communicate complex quantitative insights to technical and executive stakeholders.

Build and productionize LLM-powered agents, workflows, and RAG systems. Apply prompt engineering, embeddings, fine-tuning, and traditional ML to automate tasks and improve products. Mentor teammates and help design scalable, production-grade AI infrastructure while staying current on LLM advances.

Reviews financial services regulations and exchange rulebooks, assesses regulatory changes, and maintains auditable review records. Validates machine-learning topic classifications, provides labelled feedback, and helps maintain regulatory taxonomies. Compares platform rulebooks with authoritative sources to identify structural errors, missing provisions, duplication, and versioning issues. Performs quality assurance against development requirements and collaborates with product, engineering, and software teams to process new regulations and improve the service.

Leads biomedical data science for clinical and health research, integrating clinical, observational, EHR, genetic, wearable, electrophysiological, and video data. Develops signal-processing methods, machine-learning models, probabilistic and time-series analyses, scalable data pipelines, and reproducible workflows. Validates clinically relevant models, supports disease phenotyping and predictive modeling, ensures compliance with pharmaceutical and healthcare standards, and communicates technical findings to clients and stakeholders.

Data Science Interns use machine learning, statistics, experiments, algorithms, and data exploration to solve business and product problems. They formulate analytical approaches, interpret and validate results, collaborate with stakeholders, produce actionable insights, and help improve Microsoft products and customer experiences through peer review and iterative analysis.

Build data infrastructure and reliable pipelines, analyze product usage, train traditional and LLM-based models, define product metrics, run experiments, and translate findings into product and go-to-market decisions. The role combines data science, data engineering, product analytics, machine learning, and stakeholder communication, with ownership across data modeling, evaluation, experimentation, and product insight generation.

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.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop, train, evaluate, and deploy small task-specific ML models for real-time English conversational skills (ASR, pronunciation, grammar, recommendations). Create datasets and task-specific evaluations, improve internal SOTA, mentor engineers, and contribute to hiring.

Leads an integrated data, AI, analytics, and product engineering organization. Owns strategy, roadmaps, architecture, technical standards, production service levels, AI governance, client relationships, budgets, hiring, and organizational growth. Directs delivery of data platforms, dashboards, machine learning models, generative AI solutions, and full-stack applications while ensuring measurable business value, operational stability, responsible AI practices, and strong stakeholder governance.

Builds and maintains scalable data pipelines, data architecture, ETL infrastructure, and analytics tools. The role assembles complex datasets, improves data delivery and infrastructure scalability, supports cross-functional stakeholders, and enables actionable business insights. Required expertise includes SAS, Python, Java, MATLAB, SQL and NoSQL databases, Hadoop-based data warehousing, MapReduce, Hive, and Presto. The position requires 5–8 years of related experience and a bachelor’s degree or equivalent experience.