Top Machine Learning Jobs in India

Conduct applied research on voice-agent evaluation and self-improvement: design audio-native metrics and adversarial datasets, build observability and tracing for cascade failures, mine production traces to generate fine-tuning or prompt fixes, and validate improvements under adversarial replay. Collaborate to ship solutions and publish research.

This role involves developing scalable AI/ML models, working with cross-functional teams, following agile processes, and maintaining deployed solutions.

Develop production-ready AI applications using cloud AI services and GenAI models. Design scalable solutions leveraging LLMs, deep learning frameworks (TensorFlow/PyTorch), NLP, chatbots, and image processing. Lead and mentor teams, drive cross-team decisions, and ensure project alignment with strategic goals.

Design, build, and deploy production-ready AI applications and pipelines using LLMs and generative AI. Integrate cloud and on-prem infrastructure, apply deep learning and neural networks, and develop conversational agents and image-processing solutions while collaborating with cross-functional teams and mentoring juniors.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

The Director of Software Engineering will lead teams in developing highly available distributed systems, focusing on payment processing, AI solutions, and innovative capabilities, while ensuring product quality and team engagement.

The Senior ML Engineer will improve Avoma's Insights Engine using NLP and Deep Learning, developing algorithms, new models, and shipping them to production.

The Senior AI / ML Ops Engineer will design and operate Agentic AI systems, implement MLOps practices, and ensure production automation is secure and reliable.

The Machine Learning Engineer/Data Engineer analyzes large datasets, develops predictive models, and leads technical development while mentoring junior team members.

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.

Lead design and build AI capabilities for security products: architect RAG/LLM pipelines and agentic workflows, train and tune ML/DL models for anomaly detection and classification, implement safety guardrails and evaluation frameworks, and establish scalable MLOps (data preprocessing to production monitoring) for low-latency, secure inferences.

Design, build, and deploy scalable time-series and ML demand-forecasting models; develop discount and price-simulation tools; own full ML lifecycle including data engineering, feature engineering, deployment, monitoring, and iteration; maintain data pipelines and ensure data quality; present insights to stakeholders and collaborate cross-functionally to operationalize models.

Maintain and optimize production ML/algorithm services: debug, profile and tune for CPU/memory/I/O, add regression tests, implement parallelism, build and maintain REST APIs (Django/DRF or FastAPI), benchmark scalability and costs, ensure high code quality (tests, CI/CD, docs), and collaborate with product/data/QA to meet SLAs.

Embed with customer teams to rapidly prototype, build, and deploy GenAI-powered full-stack applications and microservices, integrating Adobe/Firefly APIs, cloud and CI/CD tooling, and reusable components while capturing field use-cases for product roadmaps.

Lead AI architecture and strategy for a next-generation cybersecurity platform: design AI runtime, RAG/agentic workflows, model selection and fine-tuning, trust and safety guardrails, co-design data/feature stores and vector retrieval, and move research into production at scale.

Build LLM-powered dashboards, RAG pipelines, AI agents, and data pipelines for retail operations. Implement model monitoring, fine-tuning, A/B testing, and ML lifecycle processes. Integrate cloud warehouses, vector DBs, and visualization tools; collaborate with stakeholders to translate retail KPIs into AI-driven operational workflows and deploy solutions for store ops, maintenance, NSO, and CEO escalations.

Design, implement, optimize, and deploy ML and NLP models into production. Perform data preprocessing, experiments, analysis, and reporting. Maintain model documentation, apply MLOps/CI-CD, and communicate findings to stakeholders while staying current on AI research and ethics.

Work with data scientists and senior ML engineers to build, train, and deploy machine learning models. Perform data preprocessing, feature engineering, model evaluation, and implement solutions using Python and common ML libraries. Follow version control and software engineering practices; exposure to cloud platforms is a plus.

Design, prototype, and deploy Generative AI solutions (RAG, fine-tuning, embeddings) using LLMs, vector databases, and cloud AI platforms. Collaborate with sales, product, and engineering to translate requirements into technical roadmaps, deliver demos/SoWs, and provide MLOps and governance guidance.

Design, develop, and deploy machine learning applications focused on embeddings and vector databases. Collaborate with teams to integrate AI/ML solutions and maintain data quality throughout the lifecycle, ensuring scalability and performance optimization of models.

Design, build, and deploy CV and NLP ML systems for document and content intelligence. Convert prototypes to production, optimize models (including LLMs), run evaluations, build scalable APIs, and document architectures and pipelines while staying current with ML research.

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.

Design, build, deploy, and maintain production-grade machine learning models and end-to-end pipelines. Perform feature engineering, model evaluation, optimization, REST API deployment, monitoring, and automated retraining. Collaborate with cross-functional teams to translate business requirements into scalable, reliable ML services using cloud, containerization, and CI/CD practices.

Design and build enterprise-scale AI search and retrieval systems using Azure AI Search and Azure OpenAI. Implement RAG/vector search, embeddings, prompt engineering, secure document-level access controls, scalable backend services, and observability to improve relevance, accuracy, and latency. Mentor engineers and drive architecture for secure, compliant enterprise RAG platforms.

Design and implement end-to-end AI/ML systems for robotic perception and control: research and build CV modules, train and benchmark models, prototype in simulation, transfer to real robots, create preprocessing/postprocessing pipelines in Python/C/C++, and optimize for real-time edge deployment.