Senior ML Operations Engineer

DeepIntent is the leading healthcare marketing platform, purpose-built to help marketers plan, activate, and optimize data-driven campaigns with speed and precision. Trusted by the world’s top healthcare brands and their agencies, DeepIntent uniquely unites media, identity, and real-world clinical data to power privacy-safe, omnichannel marketing across every screen. Backed by patented technology and proven outcomes, DeepIntent’s platform delivers measurable audience quality and script lift at scale. Learn more at www.deepintent.com . What You'll Do: DeepIntent is seeking an MLOps Engineer to join our European Data Infrastructure team. The MLOps Engineer will partner with our Data Science and AI teams to build and scale a high-performance machine learning and AI platform spanning our on-prem data centers and GPU resources - built on Python, Spark, Kubernetes, Docker, and modern orchestration frameworks (Argo Workflows, Airflow). Partner with Data Science and AI Engineering teams to adopt MLOps best practices and migrate training/inference workloads onto the platform Implement and maintain model tracking, versioning and experiment management (MLflow) with observability into model performance and drift Build CI/CD pipelines purpose-built for ML/AI artifacts (model registries, container image pipelines, automated retraining triggers) Continuously improve platform reliability, cost-efficiency and maintainability of the underlying codebase Establish monitoring and observability for ML/AI systems (Prometheus, Grafana) covering GPU utilization, model latency, throughput and custom ML metrics Design and operate ML/AI deployment infrastructure, including GPU cluster architecture, model serving and tool selection across training and inference workloads Build and maintain infrastructure for LLM and generative AI workloads, including model fine-tuning pipelines, vector databases, RAG architectures and inference optimization Collaborate with business units and Product on ML/AI-driven feature development Manage individual project priorities, deadlines and deliverables in a timely manner Who You Are: Bachelor's degree in Computer Science or similar technical field of study, or equivalent practical experience Strong software engineering skills in complex, distributed, multi-language systems (Python preferred) Hands-on experience with Spark, Docker and Kubernetes in production environments Experience building and operating end-to-end distributed systems Experience developing and maintaining ML systems built with open-source MLOps tools (e.g., MLflow, Argo, Metaflow, Airflow, Kubeflow) Strong understanding of software testing, benchmarking, and CI/CD practices Solid understanding of Linux systems administration Familiarity with LLM/generative AI tooling and concepts (model serving frameworks, embeddings, vector stores, RAG pipelines) is a strong plus Familiarity with GPU infrastructure - CUDA fundamentals, GPU…