Top Machine Learning Jobs in Delhi

Drive the Search product roadmap at Bloomreach, focusing on AI, personalization, and data-driven decisions while ensuring customer engagement and market readiness.

The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.

The role involves leading data science initiatives, mentoring team members, deploying models, and ensuring high-quality technical solutions. Responsibilities include coding, architecture design, and client requirement translation.

We are seeking a Director/VP of Sales for the USA market to develop sales strategies, lead a team, drive revenue, and build customer relationships in the cybersecurity sector.

Lead AI initiatives, craft product vision, manage lifecycle, utilize data for decisions, and foster collaboration across teams.

Lead statistical strategy and delivery for Phase I-IV clinical trials embedded with a pharmaceutical sponsor. Design analyses, support protocol and reporting activities, oversee programming/CRO work, apply SAS/R (R Shiny)/Python and ML/DL methods for analyses and automation, and mentor junior statisticians.

Lead the risk product suite roadmap and execution for merchant onboarding, fraud prevention, and credit risk. Manage a risk engineering and data science squad, build real-time risk engines and ML models, optimize KYC/KYB onboarding flows, and develop lending/settlement risk models to minimize financial loss while preserving merchant and buyer experience.

Design, build, and deploy AI agents and machine learning models; integrate ML/NLP into full-stack applications; develop APIs and data pipelines; deploy and manage services on AWS; collaborate with frontend/backend teams and maintain CI/CD, databases, and production monitoring.

Build and deploy production-grade generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, fine-tune and host LLMs, develop scalable FastAPI backends, optimize latency/cost, and containerize services for production.

Design, build, and maintain AI platform and ML pipelines by assembling LLMs and agentic workflows, integrating assistants with systems, prototyping in low-code and code, writing tests and CI/CD, and iterating with stakeholders on domusAI, domiAI, domusDocs and multi-agent frameworks.

Design, build, and scale production Generative AI systems: multi-agent workflows, advanced RAG pipelines, model fine-tuning and quantization, LLM integration and swapping, high-performance FastAPI backends, vector search and retrieval, containerized deployment, and monitoring for latency, cost, and reliability.

Design and implement end-to-end machine learning solutions: analyze use cases, build statistical and ML models, prepare and enrich data, implement scalable PySpark models in Databricks, and collaborate with product and engineering to productionize analytics for personalization and business insights.

Lead backend architecture and engineering: design scalable APIs and services, mentor engineers, write complex code, enforce best practices for databases, testing, observability, and integrate ML/LLM workloads.

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.

Lead design and production of face recognition and biometric CV systems. Build, train, optimize models (PyTorch/TensorFlow/JAX), own end-to-end ML pipelines (Airflow), ensure fairness, deploy low-latency inference on AWS, and mentor engineers.

Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.

The role seeks an AI Lead to drive Generative AI and Computer Vision projects, focusing on precise visual transformations and model training while mentoring engineering teams in best practices.

Lead design, development, and production deployment of enterprise-scale ML and GenAI solutions (LLMs, RAG, recommendation systems). Architect scalable inference and training pipelines, mentor MLEs, collaborate with stakeholders, ensure security/compliance, and measure and optimize model performance and business impact.

The Senior Machine Learning Engineer will design, develop, and deploy AI systems, focusing on Generative AI and LLM integration across various business operations, ensuring high performance and scalability.

Lead design and delivery of RAG-based chatbot and AI solutions: architect scalable Python/Java systems, deploy and manage on AWS/Azure, mentor engineers, ensure quality, and research emerging AI methods.

Design, develop, fine-tune, and productionize ML and Generative AI/LLM solutions. Build end-to-end ML pipelines (data ingestion, modeling, deployment, monitoring), implement RAG and vector DB architectures, apply prompt/context engineering and MLOps best practices, and collaborate with cross-functional teams to optimize models for performance, latency, scalability, and cost.

Design, train, and productionize state-of-the-art object detection systems. Build robust training pipelines, perform error analysis and ablations, optimize inference for latency and memory, and deliver production services with Docker, CI/CD, and APIs. Implement testing, monitoring, model versioning, and retraining pipelines. Rapidly prototype research ideas into deployable components and maintain clear technical documentation.

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.

Design, build, and deliver enterprise-grade Generative AI and AI/ML platform capabilities: reusable services, APIs, microservices, RAG and agentic solutions, model lifecycle tooling, inference and monitoring, event-driven streaming with Kafka, CI/CD and DevOps automation, and platform observability to accelerate AI adoption across business domains.

The Senior Engineer will develop and deploy machine learning models and workflows, mentor junior engineers, and collaborate with cross-functional teams to improve patient care through innovative solutions.