Top Machine Learning Jobs in Delhi

Design and implement scalable end-to-end AI/ML solutions and data pipelines; define secure cloud and on-prem architectures; lead MLOps, CI/CD, model lifecycle management, data governance, and cross-functional collaboration to drive AI initiatives in manufacturing.

Lead an 8-10 engineer team owning courier systems (dispatch, matching, pricing, ETA). Set technical direction, drive data-driven decisions and experiments, partner with data science, ensure reliability and scalability of low-latency distributed systems, and own hiring, performance, and operational excellence.

Design, build, and productionize ML models for credit scoring, fraud detection, and segmentation. Maintain model governance and documentation, develop scalable MLOps pipelines and monitoring, generate analytical insights and dashboards, and mentor junior data scientists while leading end-to-end data science initiatives.

Lead architecture and development of the core native iOS SDK and related apps. Own feature delivery from design to public release, write high-quality tested code, collaborate with R&D and ML teams, support customers to resolve issues, and manage priorities across large-scale, distributed projects.

Lead post-deployment operations and optimization of Agentic AI, LLM integrations, and Python-based analytical pipelines. Supervise a data science operations team, ensure secure high-availability production systems, manage integrations with enterprise systems (SAP, Salesforce, Postgres), and drive continuous improvement, monitoring, and adoption of analytics solutions.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Design, build, and deploy scalable ML models and pipelines from research to production. Own feature engineering, model tuning, real-time prediction, MLOps automation, monitoring, and lifecycle management to solve visibility, forecasting, and freight audit problems.

Develop, deploy, and maintain scalable AI/ML pipelines and production systems, including LLMs and generative AI models. Collaborate with data scientists, software engineers, architects, and clients to integrate AI capabilities into products. Optimize model performance, latency, and cost on AWS, GCP, and Azure, using Docker, Kubernetes, CI/CD, monitoring, versioning, and other MLOps practices.

The Data Scientist will analyze and model data for ML systems, focusing on Personalization and Recommendation Engines, and collaborate with teams to enhance data-driven solutions.

Operate and own model lifecycle for defence and strategic deployments: design serving infra, CI/CD for model updates, monitoring/observability, evaluation/A-B testing, containerised serving for edge and air-gapped environments, collaborate on eval pipelines, create runbooks, and lead incident response for production model failures.

The role involves developing enterprise applications using Java and Spring, integrating AI features, mentoring junior developers, and collaborating with cross-functional teams.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

As an AI Expert, you will review and evaluate AI-generated outputs, correct text, analyze multimedia content, and ensure cultural appropriateness and accuracy in the outputs.

As a Demo Engineer, you support pre-sales by designing AI-driven demos and proof of concepts, collaborating closely with teams to showcase Cresta's capabilities.

Train and deploy small, task-specific ML models for realtime English conversational-skill applications. Create datasets and task-specific evaluations, improve internal SOTA, develop ASR and speech-feedback metrics, iterate experiments, ship production systems, and collaborate cross-functionally while helping hire as the team grows.

Develop, deploy, and scale AI/ML and generative AI solutions for a fintech platform. Design data models and pipelines, build agentic frameworks, integrate OCR and vector DBs, implement RAG, and champion MLOps and cloud deployment practices while mentoring teammates.

The role focuses on designing AI-driven workflows, developing user-friendly interfaces for complex backend logic, and enhancing UI/UX with real-time interactions and performance optimizations.

Design, develop, and deploy enterprise AI/ML solutions including GenAI, LLM-based RAG systems, and agentic AI. Build scalable models, integrations, monitoring, and reusable frameworks while collaborating with stakeholders for production adoption and continuous improvement.

Designs, builds, and optimizes machine learning models and infrastructure, develops production-grade ML services and data pipelines, conducts research and evaluation, implements LLM fine-tuning and RAG solutions, and partners cross-functionally to translate business requirements into deployable ML deliverables.

Build and maintain Python backend services and internal tools for AI/LLM applications. Integrate LLM APIs, design scalable REST APIs and async workflows, handle prompt management and model routing, monitor performance, debug distributed systems, automate data workflows, and collaborate to productionize AI features with emphasis on scalability, reliability, and safety.

The Senior ML Research Engineer will develop computer vision algorithms for item authentication, mentor team members, and oversee model deployment.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Design, develop, and deploy deep learning and computer vision models for fraud detection (including deepfake and synthetic data detection). Own production models, research new algorithms, deploy on AWS SageMaker or devices, monitor model performance, and collaborate with engineers and product managers in an Agile environment.