Top Machine Learning Jobs in Delhi

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Build and validate production machine learning and statistical models for music and entertainment marketing. Develop real-time data systems, predictive scoring, trend detection, AI briefing classification, dashboards, and GenAI-assisted workflows. Collaborate with data engineering, paid media, and creative teams while implementing tokenization, anonymization, and cross-border data privacy controls. The role is a 12-month fixed-term contract supporting clients across APAC.

Lead international expansion of AI-enabled agricultural weather forecasting programs. Responsibilities include selecting new markets, establishing offices and teams, developing government and telco partnerships, overseeing forecast deployment, managing staff and consultants, and building replicable best practices. The role reports to the VP of Agriculture, manages senior personnel, and requires extensive collaboration with governments, researchers, delivery partners, and private-sector organizations across low- and middle-income countries.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Build production-grade AI and machine learning security products using Go services and Python pipelines. Develop LLM-powered analysis, RAG, classification, structured extraction, and agentic AI capabilities; establish evaluation datasets, metrics, regression suites, and drift monitoring. Collaborate with security researchers to translate threats into actionable detections, and deploy cloud-native solutions using Docker, Kubernetes, and major cloud platforms.

Design and develop scalable full-stack features using Java, Spring Boot, Python, FastAPI, Angular, and Azure. Lead technical decisions, backend architecture, API design, code reviews, engineering standards, deployment strategies, CI/CD improvements, cloud deployments, and observability. Collaborate with product, data science, and design teams to build AI-powered user experiences, contribute technical insights to roadmap planning, and balance hands-on coding with technical leadership.

Build privacy and anonymization systems that detect and transform PII, credentials, secrets, and other sensitive information in data used for AI training. Develop detection methods combining rules, statistical models, classifiers, and LLMs; create production data-processing pipelines; evaluate privacy risk and retained utility; and ensure robustness against schema drift, unusual formats, leakage, and adversarial re-identification.

Research Engineer applying frontier machine learning and reinforcement learning research to production AI capabilities. Responsibilities include model design, training, evaluation, RLHF, multi-objective optimization, LLM post-training, reward modeling, auditing, and translating prototypes into reliable systems. The role also involves customer engagement, technical communication, and shaping research and product strategy.

Leads commercial performance for global and regional eCommerce markets, improving revenue, margin, EBIT, conversion, AOV, demand shaping, customer experience, and retention. Partners with market leaders to diagnose performance, recommend actions, align improvement plans, govern KPI frameworks, and ensure adoption. Leads data storytellers, establishes analytical and measurement standards, develops global playbooks, and influences senior stakeholders across a global matrix organization.

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.

Lead technical support for B2B customer deployments, diagnosing issues across ML models, applications, APIs, infrastructure, networking, and deployment environments. Investigate incidents, review logs and configurations, coordinate escalations with FDE, SRE, and Engineering teams, monitor deployment health, document root causes, communicate with customer stakeholders, and develop tools and dashboards to meet support SLOs.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Own and improve the Command|Alert alerting platform, including rule evaluation, anomaly detection, event correlation, reliable Kafka and OpenSearch pipelines, and downstream notifications. Convert security and network telemetry into usable topologies and dependency maps, support LLM-based alert generation, resolve production issues, lead reliability improvements, deliver independent projects, and mentor engineers while shaping technical direction.

Drive adoption of Databricks Genie across a regional territory through technical strategy, executive engagement, workshops, presentations, demos, and customer-facing collateral. Partner with Account Executives, GTM, Product, Engineering, GSIs, and consulting firms. Architect governed analytics solutions spanning semantic models, warehouses, metric layers, Lakehouse, and AI ecosystems; address governance and security concerns; resolve complex technical escalations; and provide roadmap feedback.

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.

Design, develop, and optimize secure, scalable cloud data platforms and ETL/ELT pipelines across AWS, Azure, and GCP. Write Python and SQL, implement data governance and quality controls, optimize performance and costs, and support analytics and BI layers. Contribute to architecture, pre-sales demos, proofs of concept, and client discussions. Mentor junior developers, conduct code reviews, and collaborate with architects, QA, DevOps, and business stakeholders.

Owns data collection and annotation operations, defines product specifications and user flows, and supports apps and internal tools through delivery. Manages field teams, implementation partners, pilots, feedback loops, product metrics, and deployment improvements. The role also contributes to grant and partnership work while collaborating with engineering and ML teams. Candidates need product or delivery experience, technical fluency, data comfort, field deployment exposure, and familiarity with AI or ML products and data protection.

Own product initiative delivery from kickoff through release, coordinating Product, Engineering, Data, and Solutions. Track dependencies, risks, blockers, scope changes, timelines, testing, handoffs, and launch readiness. Maintain accurate project status and documentation while ensuring execution remains aligned with product intent. This hands-on role requires technical fluency, cross-functional communication, strong ownership, and daily overlap with an Asia-based technology team.

Lead complex technology projects from planning through closure, managing scope, budgets, schedules, resources, dependencies, risks, governance, and stakeholder communication. Coordinate engineering, product, QA, operations, consulting teams, vendors, and clients. Manage Agile execution, documentation, compliance, executive reviews, and government or public-sector engagements. Mentor teams, improve delivery processes, support pre-sales and proposals, and ensure predictable, high-quality project outcomes.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

Drive full-cycle sales for complex, high-value AI research workspace opportunities. Originate and close new business, support strategic renewals, expand stakeholder relationships, lead value-based workshops, and translate AI and research analytics capabilities into measurable customer outcomes. Engage senior decision-makers across enterprise, government, academic, and research organizations while developing pipelines, territories, business cases, negotiations, and long-term account growth.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Build and deploy end-to-end data infrastructure and customer-facing solutions for generative AI applications. Collaborate directly with technical customers and AI researchers, translate business needs into engineering solutions, rapidly experiment, and deliver scalable front-end, back-end, and infrastructure features. The role also involves influencing product direction, engineering culture, and critical projects supporting enterprise and government AI initiatives.

Leads the creation and scaling of AmeriSave’s India technology organization, including recruiting, engineering culture, architecture, software platforms, cloud infrastructure, AI initiatives, security, budgets, and global collaboration. The CTO partners with U.S. executives, oversees distributed engineering teams, drives delivery and operational excellence, and aligns technology strategy with business goals. The role requires extensive executive leadership, India team-building experience, software engineering depth, cloud expertise, and experience with AI and emerging technologies.