Machine Learning Engineer
Design, train, and productionize deep learning models for speech, audio, and computer vision domains. Own the end-to-end lifecycle from dataset curation and architecture design to deployment and performance monitoring.
20 Machine Learning Engineer jobs in India available for remote work from home. Apply for positions such as Machine Learning Engineer, Staff Machine Learning Engineer, Senior Software Engineer (Machine Learning) and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Design, train, and productionize deep learning models for speech, audio, and computer vision domains. Own the end-to-end lifecycle from dataset curation and architecture design to deployment and performance monitoring.
The Staff Machine Learning Engineer will lead the technical roadmap and architecture for AI-driven search relevance solutions. They will collaborate with cross-functional teams to design, evaluate, and productionize scalable machine learning models while mentoring engineering staff.
You will design, train, and deploy recommendation and ranking models while building the Python services to serve them at low latency. Additionally, you will manage end-to-end production deployments, including containerization, monitoring, and LLM-based feature development.
Lead the architecture and delivery of enterprise-grade agentic applications and machine learning systems integrated into Avalara's products. Own the end-to-end lifecycle of AI systems, including backend services, agent orchestration, model evaluation, and production support.
Design and develop machine learning models and solutions for client CDP implementations while driving product projects across cross-functional teams. You will also be responsible for building scalable data pipelines and participating in on-call rotations for production support.
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You will own the production infrastructure and operational lifecycle for AI and machine learning models, ensuring they are reliable, scalable, and performant. This includes building deployment pipelines, monitoring model health, and managing generative AI workloads in collaboration with the U.S.-based data science team.
You will lead a team in designing and implementing a large language model framework to power diverse applications across the company. Your role covers the entire development lifecycle, from conceptualization and prototyping to the delivery of platform features.
Design, build, and improve production-grade machine learning systems for classification, document understanding, and AI-powered automation. Contribute to GenAI platform capabilities while ensuring high reliability, observability, and security across all model-serving workflows.
You will own the end-to-end machine learning lifecycle, from research and model development to deployment and scaling in production environments. You will work directly with product teams to design algorithms for visibility, demand forecasting, and freight audit while ensuring system reliability and performance.
The Principal Machine Learning Engineer will build and optimize machine learning models while designing and maintaining the necessary infrastructure. They will collaborate with cross-functional teams to establish data pipelines and deliver production-grade ML services for the company platform.
Lead the design and scaling of production-grade computer vision systems for biometrics, including face recognition and attribute detection. Own the end-to-end ML pipeline from data ingestion and curation to low-latency deployment on AWS.
Develop and maintain advanced machine learning and computer vision models to detect fraud for identity verification. Deploy these models via AWS SageMaker or on-device and collaborate with product managers in an Agile environment.
Own the reliability, security, and safety of generative media model APIs to ensure high availability and performance. Build monitoring systems to detect ML-specific failures and lead incident response for model API outages.
Lead the design, development, and scaling of AI-powered solutions and enterprise-grade LLM systems to solve complex business challenges. Partner with stakeholders to define strategic roadmaps and mentor other machine learning engineers on design and coding standards.
The role involves leading the design and development of computer vision systems for biometrics, including rigorous fairness analysis and benchmarking of models. Responsibilities also include owning end-to-end ML pipelines from data ingestion to deployment and optimizing models for low-latency inference.
You will set the technical direction for ML-powered search and indexing capabilities while owning the end-to-end ML lifecycle. This includes designing scalable distributed systems and leading cross-team initiatives to improve search quality and content representation.
Architect and maintain the machine learning production lifecycle by designing scalable infrastructure and automation pipelines. Collaborate cross-functionally to deploy, monitor, and optimize AI models to ensure high performance and security.
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Design and deploy intelligent AI agents and machine learning models using frameworks like LangChain and TensorFlow. Integrate these AI capabilities into full-stack web applications and manage scalable data pipelines on AWS.
Develop and deploy machine learning models and build backend systems using Python. Integrate AI solutions with full-stack applications and develop REST APIs.
Lead the AI & ML Platform team to build and run infrastructure that supports generative LLMs, model serving, and data training. Focus on coaching engineers, driving technical standards, and collaborating with stakeholders to influence the platform's strategic direction.
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