Top Machine Learning Jobs in Delhi

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

Designs, experiments with, deploys, monitors, and evaluates machine learning models. Requires full-stack development involving Python, React, AWS, APIs, and SQL, along with statistical and probabilistic foundations. The role supports AI/ML initiatives in a consulting environment serving financial services and energy clients.

Develop generative AI applications and agentic workflows using Python, LLMs, RAG, vector databases, and frameworks such as LangChain, LangGraph, and LlamaIndex. Build AI agents integrating APIs, tools, databases, and enterprise services. Develop FastAPI REST APIs, apply machine learning libraries, optimize prompts and model performance, evaluate responses, detect hallucinations, and secure AI-generated code. Collaborate within SDLC and Agile development practices.

Deploys at utility customer sites to drive product adoption, observe platform usage, document UI, functionality, and analytical issues, and manage feedback with engineering. Builds relationships across customer organizations, presents demos to senior utility leaders and IAS officers, validates platform outputs against customer data, closes the loop on shipped features, and identifies expansion opportunities. Requires strong writing, technical curiosity, ownership, and comfort using AI tools.

Leads and mentors full-stack engineers while remaining hands-on with scalable web, backend, cloud, and AI-enabled application development. Owns architecture through deployment, builds CI/CD pipelines, integrates LLM and RAG capabilities, and ensures application security, performance, availability, and reliability. Responsibilities also include code review, engineering standards, cloud optimization, production monitoring, troubleshooting, cross-functional collaboration, and delivery of business-critical features.

Designs and develops content governance platforms, anti-abuse tools, rule engines, and scalable governance pipelines. Integrates recommendation systems, implements content takedown policies, and builds capabilities for content review, risk detection, policy enforcement, and effectiveness analysis. The role addresses spam, fraud, traffic diversion, low-quality content, and black- or gray-market activity while collaborating with product, operations, algorithm, and business teams.

Build and deploy production machine learning systems, including recommendation engines, semantic search, RAG pipelines, and LLM applications. Architect scalable APIs, workflows, background jobs, and data pipelines while optimizing reliability and performance. Collaborate with product and engineering teams, write tested modular code, conduct code reviews, and stay current with AI/ML and cloud-native technologies.

Design, build, deploy, monitor, and maintain machine learning, LLM, and multi-agent AI systems for healthcare operations. Develop and fine-tune generative AI solutions, Python/FastAPI microservices, REST APIs, and Kubernetes-based services on GCP. Conduct AI research, contribute to scientific publications, optimize production systems, and ensure privacy, security, ethical, and regulatory compliance while collaborating with cross-functional stakeholders.

Fine-tune and optimize small language models using Hugging Face, TRL, and adapter methods. Build MLOps pipelines, deploy models to edge and mobile environments, meet strict latency targets, and monitor production accuracy, latency, and hardware utilization. Evaluate model quality through benchmarking and custom evaluation suites while applying quantization, pruning, and knowledge distillation.

Lead end-to-end ML systems: data pipelines, model development (transformer-based detection), sim-to-real synthetic data, multi-sensor fusion, on-device real-time inference (Jetson), MLOps (experiment tracking, versioning), and production deployment and monitoring. Set ML roadmap and hire/lead as the team grows.

Lead the technical roadmap, architecture, development, evaluation, and productionization of machine learning and AI solutions for search relevance. Build models using queries, product catalogs, and behavioral data; integrate scalable ML components into cloud systems; analyze search quality; mentor engineers; and influence cross-functional engineering and product decisions.

Leads analysis, mining, modeling, and visualization of large-scale transactional, operational, and customer data. Partners with compliance teams to prioritize analytical use cases, support regulatory requirements, improve business efficiency, and translate complex findings into actionable recommendations. Oversees multidisciplinary analysts, manages stakeholder data requests, assesses business changes, and develops analytical systems using machine learning, big data technologies, and visualization tools.

Lead the architecture and development of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic AI systems for intelligent networking. Drive the full software lifecycle from design through production operations, establish technical direction, mentor engineers, and improve system reliability, security, observability, performance, and scalability.

Lead the technical vision, architecture, development, testing, deployment, and operations of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Build secure, resilient, observable services processing high-volume network data; guide technical strategy, improve engineering practices, and mentor engineers. Translate complex technical challenges into production-ready solutions across intelligent networking, cloud platforms, analytics, and automation.

Lead the architecture and development of scalable, secure, observable AI infrastructure, distributed microservices, real-time data pipelines, and agentic systems for intelligent networking. Drive technical vision, software delivery, system reliability, and operational excellence while mentoring engineers. Build production platforms processing high-volume network and operational data, integrating cloud infrastructure, machine learning, telemetry, and automation capabilities.

Lead the architecture, development, testing, deployment, and operation of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Define technical vision, build secure and observable high-performance services, process large-scale network data, and integrate intelligent networking capabilities. Mentor engineers, lead technical discussions and reviews, improve system reliability and performance, and translate complex technical challenges into production-ready solutions.

Develop and deploy production machine learning systems for fall detection and health risk scoring. Build classical ML models, engineer features from radar and point-cloud data, contribute to pose estimation, and create Databricks training and inference pipelines. Own model evaluation and false-alarm reduction for a safety-critical system, monitor deployed models, and mentor team members while working in a fast-moving environment.

Own end-to-end machine learning for ecommerce discovery, including recommendation and ranking models, low-latency Python serving, batch pipelines, A/B testing, and production deployments. Operate containerized services on Kubernetes, monitor systems, and extend LLM-powered shopping and catalog applications. The role requires strong recommender systems, search ranking, embeddings, vector databases, streaming, and production ML experience.

Build and operate production machine learning and generative AI infrastructure. Responsibilities include automating training and deployment pipelines, managing Kubernetes-based model serving, tracking models and data versions, monitoring drift and performance, optimizing inference with ONNX and TensorRT, supporting LLMOps and vector databases, and implementing security controls. Requires strong Python, cloud, container, ML framework, SQL, distributed systems, and GPU expertise, plus a mandatory ML certification.

Use data and advanced analytics to solve complex business problems, design and deploy scalable ML/data products on cloud platforms, collaborate with clients and cross-functional teams, and communicate actionable insights.

Design and implement a unified orchestration layer coordinating vendor and internal LLMs and human agents. Build production-grade Python services, drive ML projects from design to delivery, ensure secure, scalable AI-enabled customer experiences, and collaborate across teams to meet security, compliance, and performance standards.

Develop, train, deploy, and maintain machine learning and generative AI solutions, including predictive models, NLP applications, chatbots, LLMs, and agentic AI systems. Design scalable ETL pipelines, optimize databases, process large datasets with Spark and Snowflake, and create business visualizations. The role requires expertise across Python, SQL, cloud AI platforms, machine learning frameworks, databases, and data engineering.

Manage strategic enterprise customer relationships, serving as the primary contact for technical support, training, issue resolution, and executive engagement. Partner with sales on account health, growth, and presales activities while advocating for customer needs internally. Apply data analytics, visualization, AI tools, and automation to improve reporting, insights, troubleshooting, and customer value. Support connectivity-focused products and regularly meet clients and attend company functions.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.