Senior AI Engineer I

MetLife MetLife · Insurance

Senior AI Engineer responsible for developing and managing scalable enterprise AI capabilities, designing and training ML/DL models, integrating AI into business applications, and maintaining CI/CD pipelines for ML models. The role involves working with GenAI, RAG, LLMs, and cloud platforms.

What you'd actually do

  1. Develop and manage scalable enterprise AI capabilities that support the AI platform.
  2. Design, train, and optimize machine learning and deep learning models for a variety of business use cases (e.g., SLM, computer vision, predictive analytics, recommendation systems).
  3. Enable seamless integration of AI capabilities into business applications and workflows through APIs, SDKs, and microservices.
  4. Collaborate with multiple partners from Business, Technology, Operations and D&A capabilities (Data Governance, Data Quality, Data Modeling, Data Architecture, Data science, DevOps, BI & insights)
  5. Develop and maintain CI/CD pipelines for ML models, ensuring automated testing, versioning, deployment, and monitoring in production environments.

Skills

Required

  • Python
  • Statistics, hypothesis testing, Feature engineering, Modeling
  • Machine learning frameworks: SCikit-learn, Tensorflow, Pytorch
  • Natural language processing (NLP): spacy, Transformers, OCR
  • Gen AI: Developing, and deploying Generative AI (GenAI) solutions using large language models (LLMs)
  • Expertise with Retrieval-Augmented Generation (RAG),architectures, including integrating external data sources and vector databases to enhance LLM outputs.
  • Cloud platforms ( Azure), containerization (Docker, Kubernetes), and orchestration tools
  • Communication skills, analytical skills, structured problem-solving skills.
  • Storytelling skills ,Partner & Stakeholder engagement experience

Nice to have

  • Banking Financial Services and Insurance domain knowledge
  • Stay abreast of the latest trends and advancements in AI, ML, and platform engineering. Evaluate and prototype emerging technologies for inclusion in the enterprise platform.
  • Optimize platform components for efficiency, scalability, and reliability using best practices in distributed computing, resource management, and cloud-native architectures.
  • Advocate for security, privacy, ethical, and compliance best practices throughout the AI/ML platform and model development lifecycle.

What the JD emphasized

  • Develop and manage scalable enterprise AI capabilities that support the AI platform.
  • Design, train, and optimize machine learning and deep learning models for a variety of business use cases (e.g., SLM, computer vision, predictive analytics, recommendation systems).
  • Expertise with Retrieval-Augmented Generation (RAG),architectures, including integrating external data sources and vector databases to enhance LLM outputs.

Other signals

  • Develop and manage scalable enterprise AI capabilities that support the AI platform.
  • Design, train, and optimize machine learning and deep learning models for a variety of business use cases (e.g., SLM, computer vision, predictive analytics, recommendation systems).
  • Enable seamless integration of AI capabilities into business applications and workflows through APIs, SDKs, and microservices.
  • Develop and maintain CI/CD pipelines for ML models, ensuring automated testing, versioning, deployment, and monitoring in production environments.