Machine Learning Engineer

• Design, develop, deploy, monitor, and maintain proprietary ML models, LLMs, and multi-agent AI systems for healthcare applications
• Develop AI solutions that improve healthcare operations, workflows, efficiency, and service delivery
• Customise and fine-tune open-source LLMs and integrate enterprise LLM platforms for healthcare-specific requirements
• Develop prompting strategies to improve LLM performance across complex healthcare use cases
• Build AI solutions for workflows such as prior authorisation and other healthcare operational processes
• Develop scalable, secure, and maintainable Python microservices using FastAPI
• Design and implement RESTful APIs and backend services supporting ML and AI applications
• Deploy and orchestrate services using Kubernetes, focusing on reliability, scalability, security, and operational performance
• Work with GCP infrastructure to deploy and manage production AI and ML workloads
• Monitor model and service performance and optimise reliability, latency, scalability, and resource utilisation
• Research emerging AI and ML techniques applicable to healthcare and translate relevant research into practical solutions
• Conduct independent technical research and contribute to scientific publications and research papers
• Develop intelligent simulation systems that emulate or automate service-led workflows to achieve efficiency and cost improvements
• Collaborate with product, engineering, healthcare, and other stakeholders to translate requirements into effective AI solutions
• Ensure AI systems follow ethical, privacy, security, and healthcare regulatory requirements
• Maintain technical documentation and communicate AI concepts, system capabilities, limitations, and outcomes to technical and non-technical stakeholders
• Contribute to continuous improvement of AI engineering practices, model development processes, and production infrastructure

ML Engineer

• Build and automate ML pipelines for training, deployment, monitoring, and retraining.
• Deploy and manage machine learning solutions on cloud platforms, preferably Azure.
• Implement model monitoring, governance, versioning, and performance tracking.
• Collaborate with Data Science and Engineering teams to productionize ML models.
• Manage cloud-based ML services and infrastructure.
• Improve MLOps platforms, tools, and deployment practices.
• Work with stakeholders and technology partners to deliver scalable ML solutions.

MLOps Engineer

• Design, develop, and maintain stable and reliable AI/ML Ops platforms and pipelines
• Package and deploy AI/ML services to production, ensuring reproducibility and interpretability
• Design and implement automated CI/CD pipelines to accelerate model deployment
• Provision and optimize infrastructure for model training and serving using Docker, Kubernetes, or serverless platforms
• Implement post-deployment monitoring for model performance, data drift, and latency
• Automate retraining and data pipeline workflows
• Manage deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation stacks, including vector databases and knowledge graphs
• Optimize GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference
• Collaborate with data scientists, data engineers, and software engineers to connect model development with production
• Manage versioning for data, code, and models using tools such as MLflow
• Implement data security measures and ensure compliance with data governance policies
• Evaluate emerging data technologies and identify infrastructure innovation opportunities
• Diagnose and resolve complex data-related issues
• Perform other duties as assigned

AI ML Ops Software Engineer

• Design, develop, and maintain scalable data pipelines, BigQuery datasets, SQL transformations, and scheduled workflows powering Teleport's AI and Machine Learning products.
• Build, deploy, and optimize Machine Learning models and Applied AI solutions.
• Collaborate with senior engineers to productionize forecasting models, AI agents, Retrieval-Augmented Generation (RAG) systems, and Large Language Model (LLM) applications.
• Work with Product, Data, and Engineering teams to translate business problems into reliable, production-grade AI capabilities.
• Ensure high-quality data, robust engineering practices, continuous model evaluation, and operational excellence.
• Own scope end-to-end, make fast data-informed decisions, and maintain reporting, documentation, and processes.

AI/ML Engineer

• Own the machine learning powering product discovery for merchants
• Train, build, deploy, and operate models and their serving services end to end
• Design, train, and ship recommendation and ranking models, including session-based embeddings, collaborative filtering, and content and visual embeddings
• Build and operate low-latency Python services serving the models
• Run batch pipelines that rebuild model artifacts daily across hundreds of merchants
• Design and analyze A/B tests and make shipping decisions based on evidence
• Own deployments, including containers, Kubernetes manifests, autoscaling, dashboards, and alerts
• Extend the shopping assistant and LLM-assisted catalogue enrichment with proper evaluation
• Optimize live storefront recommendation re-ranking under a 200 ms budget, measured by merchant conversion

Senior Software Engineer – Machine Learning

• Design, develop, and deploy production-grade machine learning and Generative AI applications
• Build and integrate LLM-powered applications, intelligent agents, and AI automation workflows
• Develop scalable backend services and APIs using Python and FastAPI
• Develop, evaluate, and improve AI systems using modern ML frameworks and technologies
• Design and implement AI/ML pipelines covering experimentation, evaluation, deployment, monitoring, and optimization
• Integrate foundation models and LLM APIs into production applications
• Build reliable AI systems capable of handling complex, multi-step workflows
• Deploy and scale ML applications using cloud infrastructure and containerized environments
• Collaborate with engineering and product teams to translate business problems into practical AI solutions
• Evaluate model performance, identify failure modes, and improve accuracy, reliability, latency, and cost
• Contribute to technical architecture decisions across ML systems, APIs, infrastructure, and deployment
• Take ownership across the complete development lifecycle in ambiguous environments

• Design and automate end-to-end ML pipelines for continuous training and continuous deployment using Kubeflow, MLflow, or AWS SageMaker Pipelines
• Orchestrate containerized model deployments on Kubernetes using KServe and Triton Inference Server
• Configure low-latency inference endpoints and auto-scaling GPU/CPU clusters
• Implement model tracking and data versioning foundations, including feature stores, model registries, and DVC
• Build automated AI performance and data monitoring gates for model accuracy decay, data drift, concept drift, and processing latencies
• Optimize inference environments using ONNX, TensorRT, and quantization strategies
• Integrate generative AI and LLM operational frameworks with semantic caching, vector database scaling, and prompt validation pipelines
• Govern machine learning access controls and security profiles, including data separation, model access tokens, and encryption protocols

AI/ML Ops Engineer

• Define and own the technical architecture for AI-enabled .NET and React applications
• Lead a team of engineers through the full software development lifecycle, from design to production
• Collaborate with product, data science, and UX teams to translate AI concepts into usable features
• Implement best practices for code quality, testing, CI/CD, and security
• Mentor junior developers and promote continuous learning of new frameworks and tools
• Evaluate and integrate emerging technologies such as generative AI models and Vibe coding
• Drive performance optimization for backend services and front-end interfaces
• Ensure compliance with enterprise standards and GovCon regulatory requirements
• Participate in code reviews, technical workshops, and architecture forums
• Contribute to the product roadmap by providing technical insights and feasibility assessments

Lead Software Engineer, ML Engineering

• Design, train, and evaluate threat detectors using classifiers, embedding-based models, fine-tuned LLMs, and rule/ML hybrid approaches
• Build and maintain training and evaluation datasets, labeling workflows, and benchmark suites
• Track model performance through precision/recall, error analysis, drift, and adversarial robustness
• Serve models in production under strict latency requirements
• Build inference services in Go and integrate them with gateway and endpoint pipelines
• Optimize inference performance and cost through quantization, distillation, batching, and caching
• Work with security researchers to turn emerging attack techniques into training data and detection logic
• Build and maintain MLOps workflows for reproducible training, model registries, monitoring, and safe model rollouts
• Use AI-assisted development workflows for implementation, testing, debugging, and experimentation

ML Engineer – Threat Detection Models

• Architect, implement, and maintain scalable infrastructure to train, fine-tune, and deploy state-of-the-art AI models
• Collaborate with Research Scientists to translate theoretical concepts and prototypes into clean, modular, performant codebases
• Optimize model inference and training workflows for memory, speed, and cost efficiency using quantization, pruning, distillation, and distributed execution
• Design resilient data pipelines, dataset curation workflows, and automated evaluation suites
• Build monitoring, logging, and continuous deployment systems for ML to track model performance, drift, and production system health
• Conduct code reviews, mentor junior engineers, and drive technical best practices within the AI Science organization
• Collaborate with Data Engineers and Platform Engineers on advanced ML models and AI infrastructure
• Turn complex algorithmic innovations into robust, scalable, low-latency production applications

Machine Learning Engineer jobs in India

ML Engineer

MLOps Engineer

AI ML Ops Software Engineer

AI/ML Engineer

Senior Software Engineer – Machine Learning