MetLife

Role Value Proposition:

As an AI & Data Science Engineer focused on enterprise AI platform development, you will play a pivotal role in architecting, designing, and implementing the core AI and machine learning (ML) infrastructure that underpins our next-generation business applications. You will collaborate with cross-functional teams--including enterprise platform and architecture team, data engineers, DevOps, and business stakeholders--to deliver solutions that are not only technically sound but also aligned with the strategic goals of the organization. 
 

Key Responsibilities:

• AI Platform Capability Development: Design, develop, and manage scalable enterprise AI capabilities that support the AI platform.
• Model Engineering: 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).
• Data Pipeline Engineering: Collaborate with data engineers to design and implement robust data ingestion, transformation, validation, and feature engineering pipelines tailored for AI/ML workloads.
• Platform Integration: Enable seamless integration of AI capabilities into business applications and workflows through APIs, SDKs, and microservices.
• Automation & MLOps: Develop and maintain CI/CD pipelines for ML models, ensuring automated testing, versioning, deployment, and monitoring in production environments.
• Performance & Scalability: Optimize platform components for efficiency, scalability, and reliability using best practices in distributed computing, resource management, and cloud-native architectures.
• Security & Compliance: Implement and advocate for security, privacy, ethical, and compliance best practices throughout the AI/ML platform and model development lifecycle.
• Innovation & Research: 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.
• Documentation & Knowledge Sharing: Produce clear technical documentation and participate in knowledge sharing to mentor team members and enable collaboration across the organization.

Essential Business Experience and Technical Skills:

Required:

• Bachelor’s or Master’s degree in computer science, Data Science, Engineering, Mathematics, or a related field.  A PhD is a plus.
• 7+ years of experience in AI/ML engineering, GenAI, data science, or related roles.
• Proficiency in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, XGBoost, Hugging Face).
• Strong software engineering skills with experience in Python (required).
• Proven experience designing, developing, and deploying Generative AI (GenAI) solutions using large language models (LLMs) such as GPT, Llama, Claude, etc.
• Experience with modern GenAI frameworks and libraries (e.g., LangChain, LlamaIndex, Hugging Face Transformers).
• Familiarity with best practices for responsible AI, including data privacy, bias mitigation, and model monitoring.

Preferred:

• Experience with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and orchestration tools.
• Hands-on expertise with Retrieval-Augmented Generation (RAG) architectures, including integrating external data sources and vector databases to enhance LLM outputs.
• Strong understanding of prompt engineering, fine-tuning, and evaluation of generative models for real-world applications.
• Ability to build, optimize, and scale GenAI pipelines for tasks such as document Q&A, summarization, chatbots, and knowledge retrieval.

At MetLife, we’re leading the global transformation of an industry we’ve long defined. United in purpose, diverse in perspective, we’re dedicated to making a difference in the lives of our customers.