Lead Machine Learning Engineer – Agentic AI
Lead Machine Learning Engineer – Agentic AILocation: 100% RemoteIndustry: InsurancePosition OverviewWe are seeking a Lead Machine Learning Engineer with strong experience in Agentic AI, Generative AI, Machine Learning, MLOps, and cloud-based engineering to design, develop, and deploy intelligent AI solutions for an insurance industry client.The ideal candidate will be a hands-on technical leader who can architect production-grade ML and Agentic AI systems, work closely with Data Scientists, Data Engineers, DevSecOps and Product teams, and mentor engineering team members.Key ResponsibilitiesArchitect, design, develop, test, and deploy Machine Learning and Agentic AI systems for complex business problems.Build production-ready LLM/GenAI and AI agent solutions, including data, ingestion, memory, and observability pipelines.Develop application code and ML models using Python, SQL, and C++ as applicable.Design scalable cloud-based ML architectures and optimize models for production workloads.Build RESTful APIs, microservices, and distributed applications supporting AI/ML solutions.Implement CI/CD, automated testing, model deployment, monitoring, and operational best practices.Develop and manage ML pipelines using technologies such as MLflow, AWS SageMaker, GitHub Actions, Jenkins, or CloudBees.Implement model monitoring, validation, drift detection, bias detection, explainability, performance monitoring, and Agentic AI observability.Develop data transformation and processing pipelines using Spark/PySpark and other data engineering technologies.Work with relational, NoSQL, graph, structured, semi-structured, and unstructured data.Partner with Data Science, Data Engineering, DevSecOps, Product, and Analytics teams to deliver scalable AI products.Provide technical leadership, remove complex technical impediments, and coach other engineers.Stay current with emerging AI/ML technologies and continuously identify opportunities for innovation.Required QualificationsStrong hands-on experience in Machine Learning Engineering and production ML systems.Proven experience building Generative AI / LLM / Agentic AI solutions.Strong Python programming skills; SQL required.Experience with ML/DL frameworks such as PyTorch, TensorFlow, or Scikit-learn.Strong understanding of ML algorithms, neural networks, NLP, predictive analytics, and model development.Experience with MLOps, CI/CD, model deployment, monitoring, and governance.Experience with AWS or other major cloud platforms and cloud-based ML architectures.Experience with MLflow, SageMaker Pipelines, or equivalent ML pipeline frameworks.Experience with Spark/PySpark and distributed data processing.Strong software engineering fundamentals including object-oriented programming, REST APIs, microservices, version control, testing, and distributed systems.Experience working with relational, NoSQL, and/or graph databases.Strong problem-solving, communication, collaboration, and technical leadership skills.Experience with Agile development methodologies.Preferred QualificationsExperience with Agent Development Life Cycle (ADLC).Experience with agent memory, context management, agent orchestration, and AI observability.Experience with LLM development tools such as Claude Code or similar AI-assisted development tools.Experience with GitHub Actions, Jenkins, or CloudBees.C++ development experience.Experience with NLP and/or Computer Vision.Insurance or financial services industry experience.Bachelor's degree in Computer Science, Engineering, or a related technical field.Key SkillsAgentic AI | Generative AI | LLM | Machine Learning | Deep Learning | Python | AWS | MLOps | MLflow | SageMaker | PyTorch | TensorFlow | Scikit-learn | Spark | PySpark | SQL | REST APIs | Microservices | CI/CD | Model Governance | AI Observability | Data Pipelines