• Responsible for developing and operationalizing AI solutions, with a focus on scalability, governance and the industrialization of models. • Act as the technical reference for AI and MLOps, leading the construction, deployment and evolution of models in production. • Define the architecture and standards for the AI and MLOps pipeline. • Develop and operationalize models (predictive and generative). • Design ML/LLMOps pipelines (training, deployment and monitoring). • Implement CI/CD, version control and model monitoring. • Ensure governance, traceability and quality. • Translate business requirements into technical solutions.

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives. • Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain. • Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration). • Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid). • Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services. • Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT). • Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy. • Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots. • Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs. • Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.

• Architect and integrate hybrid AI systems. • Design and deploy scalable AI architectures. • Lead the full AI/ML lifecycle. • Engineer event-driven data pipelines and feature stores. • Ensure Responsible AI practices. • Implement and maintain MLOps pipelines. • Transition R&D prototypes into production. • Provide technical leadership and mentorship. • Collaborate across engineering, data, and modeling teams. • Support proposal and solution development.

Role Description We are seeking a Sr MLOps Engineer to build the model lifecycle, deployment, observability, and infrastructure foundations used by multiple production AI features, including recognition, AI Video Search, Multimodal AI, Agentic AI, and Energy AI ship faster with shared, reliable platform primitives. In this role, you will be responsible for: - Build model registry, model serving, deployment, rollback, and CI/CD systems for production AI services. - Own feature, dataset, model, and prompt versioning patterns across AI products. - Standardize training, evaluation, release, monitoring, and operational workflows for AI teams. - Improve reliability, cost efficiency, latency, and repeatability of AI launches. - Create reusable platform patterns across AI features. - Partner with engineering, data science, product, and operations teams to productionize AI capabilities at scale. Qualifications - Bachelor’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field, and 5+ years of professional experience in software development, applied science, or ML engineering; or - Master’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field, and 2+ years of professional experience in software development, applied science, or ML engineering. - Experience building production ML platforms, model serving systems, or MLOps workflows. - Strong Python and cloud engineering skills. - Experience with CI/CD, Git, infrastructure-as-code, and production monitoring. - Familiarity with model registry, feature/data versioning (DVC), validation, deployment, rollback, and observability. - Ability to communicate tradeoffs clearly across engineering, data science, and product teams. Requirements - Experience with GCP/AWS, Cloud Run, Kubernetes, Vertex AI, SageMaker, MLflow, or equivalent tools. - Experience with AI services for computer vision, LLMs, multimodal models, or recommendation systems. - Experience with data validation, dataset versioning, feature stores, or model quality monitoring. - Experience optimizing cost, latency, reliability, and operational readiness for AI systems. - Experience with IoT, edge AI, smart home, or distributed device environments. Benefits - Paid holidays and flexible paid time away. - Employee/Friends/Family Discounts. - Medical/dental/vision/life coverage & 24/7 Medical Hotline. - 401(k) + Employer Match. - Employee Resource Groups.