Top Machine Learning Jobs in Delhi

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

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.

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.

Performs AI data collection, evaluation, annotation, pairwise comparisons, counting, and object tagging across audio, video, images, and other content. The role supports improving AI-generated content in Tamil and requires native-level Tamil fluency, advanced English proficiency, and preferably experience with machine learning data tasks, preprocessing, quality assurance, or labeling.

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.

Performs Marathi AI data tasks, including collecting, evaluating, annotating, comparing, counting, tagging, and labeling audio, video, images, and other content. Requires native-level Marathi fluency and fluent or advanced English proficiency. Prior experience with machine learning tasks, data preprocessing, quality assurance, or data annotation is preferred. This is a flexible, part-time freelance work-from-home role.

Build and improve forecasting, capacity, staffing, revenue, workforce, and strategic planning models. Translate business and financial assumptions into integrated forecasts, conduct scenario and sensitivity analyses, validate models, assess risks and tradeoffs, and communicate recommendations to executive stakeholders. The role also establishes modeling standards and mentors analysts.

Evaluates and develops AI and computational science manuscripts for publication, makes editorial decisions, shapes open-science policies, and advises authors on clarity and reporting standards. The editor recruits and supports external editors, commissions reviews and commentary, attends conferences and research meetings, visits institutions, and promotes published work through press releases and research highlights. The role requires PhD-level machine learning or related research experience, strong scientific judgment, broad interdisciplinary knowledge, and excellent English communication skills.

Own the vision, roadmap, backlog, adoption, and continuous improvement of an internal corporate GenAI platform. Translate stakeholder and user needs into product requirements, coordinate agile delivery and user acceptance testing, manage reporting and communications, and support training and adoption. The role also manages project budgets, approvals, audit documentation, governance submissions, and administrative coordination while ensuring alignment with AI safety, privacy, and enterprise security considerations.

Own the strategy, roadmap, development lifecycle, and go-to-market activities for fraud analytics products. Lead customer discovery, prioritize product improvements, analyze complex data, collaborate with development and data science teams, and use market and customer insights to improve user outcomes. The role requires fraud prevention expertise, statistical modeling and AI/machine learning knowledge, strong stakeholder communication, and flexible UK-US working hours.

Leads global sales operations, strategy, go-to-market planning, governance, analytics, forecasting, incentive programs, and process improvement. The role drives sales digitalization, AI-powered efficiency initiatives, pipeline management, reporting, organizational transformations, and sales competency development across a global, matrixed organization.

Designs and executes testing strategies for AI and machine learning applications. Responsibilities include building test automation frameworks, functional and regression testing, API and data-flow validation, test case and documentation development, root cause analysis, and collaboration with development teams to embed quality checks throughout the software delivery lifecycle.

Develop predictive models and machine learning algorithms, preprocess and explore data, create visualizations, and optimize model performance in production. Use Python to build scalable data pipelines and analytical tools, translating complex data into actionable business insights and strategic recommendations. The role requires strong statistical, analytical, and problem-solving abilities, with responsibility for supporting data-driven decision-making and operational efficiency.

Leads the design, development, validation, monitoring, and production deployment of AI and machine learning solutions. Provides technical direction and mentorship to data scientists and engineers, translates business needs into scalable architectures, builds Python-based ML pipelines, resolves complex technical issues, and drives high-quality hands-on delivery across the full model lifecycle.

Develops, deploys, and optimizes scalable machine learning models and pipelines. Performs data analysis, applies deep learning to complex data, monitors production model performance, and collaborates with product and engineering teams to integrate AI capabilities into business solutions.

Lead the architecture, deployment, automation, monitoring, and lifecycle management of scalable machine learning systems. Establish model versioning and ML-specific CI/CD pipelines, improve production reliability, and optimize workflows. Provide technical leadership and mentorship while coordinating with data scientists and software engineers to deliver robust machine learning solutions.

Leads advanced analytics and machine learning initiatives from project scoping through production deployment. Responsibilities include stakeholder management, feature engineering, model evaluation, optimization, production monitoring, drift detection, and scalable model integration. The role requires strong Python skills, statistical modeling expertise, analytical problem-solving, and the ability to communicate technical insights to non-technical stakeholders.

Designs and executes testing strategies for AI and machine learning applications. Responsibilities include building AI-focused test automation frameworks, functional and regression testing, test case development, API validation, test documentation, test plan execution, and root cause analysis of defects.

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.

SLB seeks graduate-level digital technology professionals for roles spanning artificial intelligence, software engineering, data engineering and science, machine learning, IIoT, high-performance computing, QA, software security, and UX design. Employees collaborate across disciplines in global technology centers, leveraging cloud computing, advanced data ecosystems, connectivity, and emerging technologies to develop solutions for the energy industry. The program includes training and professional development.

Lead cybersecurity trials and proof-of-concept engagements, translating technical findings into business value for executives and security teams. Research US regional cyber threats, create intelligence briefs and exposure reports, support partners with onboarding and co-selling, and contribute to thought leadership through events, webinars, workshops, and research-driven content. The role requires strong threat intelligence, offensive security, consulting, communication, presentation, collaboration, and self-direction skills.

Leads actuarial pricing initiatives for homeowners insurance, including rating plans, indication frameworks, advanced pricing models, segmentation strategies, regulatory filings, and model governance. Mentors actuarial analysts, investigates segmentation improvements, provides strategic recommendations to leadership, and collaborates with Product, Reserving, Data Science, Underwriting, Sales, and Claims. Requires advanced Excel, SQL, Python or R, insurance pricing experience, and progress toward actuarial credentials.

Build, implement, monitor, and optimise IFRS9 credit risk, statistical, machine learning, and automated lending decisioning models. Responsibilities include data preparation, feature engineering, validation, monitoring, recalibration, root-cause analysis, policy optimisation, model deployment, documentation, and stakeholder collaboration across credit, finance, underwriting, data, and engineering teams.

Conduct security research involving malware, threats, vulnerabilities, adversary tracking, threat intelligence, detection, remediation, and reverse engineering. Analyze threat trends, process intelligence reports, identify security defects, contribute code and automation, support incident response, and use research results to improve security products and services. The role focuses on cloud-scale threat detection using machine learning and behavioral profiling.

Drive adoption and value realization of Neara’s digital-twin platform among Japanese utility customers. Responsibilities include customer engagement, platform enablement, training, ROI and business-case development, stakeholder management, solution expansion, and collaboration with Sales, Deployment, Product, and Engineering teams. The role requires translating customer strategic goals into technical solutions and measurable business outcomes, with regular customer and team travel in Japan and internationally.