Remote Software engineering Jobs · MLOps

• Design, train, and operate ML systems that power the Tebra platform.
• Own the entire lifecycle from data exploration and model development to deployment.
• Push the boundaries of applied ML in healthcare, transforming messy real-world data into reliable automation.

• Lead a cross-functional team of ML engineers, data scientists, and MLOps professionals.
• Oversee the full lifecycle of LLM and ML projects — from data collection to training, evaluation, and deployment.
• Provide technical direction on large-scale model training, fine-tuning, and distributed systems design.

This role involves hands-on design, development, and deployment of enterprise-grade agentic AI solutions. You will work on multi-agent systems, workflow automation, and AI integration across business processes. The position offers exposure to cutting-edge AI technologies and collaboration with cross-functional teams.

• Implement the company’s AI strategy to set the foundation for AI initiatives.
• Lead the design, development, and rollout of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) based solutions.
• Develop and deploy AI models and solutions into the company’s cloud infrastructure.

The AI Engineer develops and deploys agentic AI solutions for clients. Implements components for document processing, workflow automation, data retrieval, and structured output generation. Contributes to monitoring, logging, metrics, and guardrail configurations for agentic systems.

The AI Architect will drive Magna’s AI vision from concept to commercialization. You will design and implement AI systems and lead development teams. You will also partner with executives to identify opportunities, drive adoption, and deliver measurable value.

Lead AI and ML initiatives to design and implement production-grade machine learning systems and pipelines. Develop scalable infrastructure for model training, evaluation, and deployment, ensuring reliability and observability. Collaborate with cross-functional teams to drive innovation and efficiency.

• Design, build, and productionize ML models for fine-tuned, Retrieval-Augmented Generation (RAG), and generative AI features.
• Build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.
• Collaborate with product and engineering to iterate on datasets, evaluation metrics, and model architectures to improve quality and relevance.

Design, implement, test, and deploy offline object detection, tracking, and fusion modules to automatically create annotations on Cloud Services from recorded sensor data. Define and implement the ingestion, preparation, curation, and governance of large multidimensional datasets in support of analytical models and workflows. Evaluate and make recommendations regarding technical advances that improve productivity and quality, reduce turnaround time and strengthen operational reliability.

• Lead AI initiatives and develop machine learning models to leverage organizational data effectively.
• Design, build, and maintain scalable ML infrastructure and pipelines for model training, evaluation, and deployment.
• Collaborate with Product, Clinical, Executive, Design, and Data teams to integrate ML solutions into workflows.