Remote Sr MLOps & Gen AI Engineer

& Experience 5+ years of experience building and deploying software, ML systems, or AI platforms 1+ year of hands-on experience with Generative AI / LLM-based applications in production Strong programming experience in Python Experience with ML/LLM frameworks (PyTorch, TensorFlow, Hugging Face, etc.) Hands-on experience with: RAG architectures Vector databases (Pinecone, Weaviate, FAISS, etc.) Embeddings, prompt engineering, and LLM orchestration Experience deploying solutions in cloud environments (AWS, Azure, or GCP) Strong understanding of APIs, microservices, distributed systems, and scalable backend design Experience with Kubernetes, containers, and cloud-native infrastructure Proven background building CI/CD pipelines and MLOps workflows Experience with monitoring, observability, and alerting for AI/ML systems Strong understanding of ML lifecycle management, versioning, and production operations Ability to design secure, scalable, production-grade AI systems Strong communication and cross-functional collaboration skills

Skills & Experience Experience in healthcare environments (especially EPIC or similar platforms) Knowledge of HIPAA, PHI, and healthcare compliance

Background in AI governance, model evaluation, and responsible AI frameworks Experience optimizing GPU usage and large-scale inference workloads Familiarity with agent-based AI systems or autonomous workflows Experience with real-time data pipelines, event-driven systems, or streaming architectures Exposure to fine-tuning techniques (LoRA, PEFT, RLHF, etc.) Experience building or contributing to enterprise AI platforms or internal developer tools Prior experience mentoring engineers or leading technical initiatives

Insight Global is looking for a Senior MLOps & Generative AI Engineer to help build and scale enterprise AI capabilities across the organization. This role sits at the intersection of MLOps infrastructure and Generative AI application development, supporting initiatives that enhance healthcare outcomes and operational efficiency. This person will partner closely with data scientists, engineers, architects, and product teams to bring AI models from experimentation into secure, scalable production environments.