MetLife

Introduction

Are you passionate about harnessing the power of artificial intelligence and data science to solve real-world business challenges? Do you thrive on building robust, scalable capabilities that deliver measurable value? We are seeking a skilled and motivated Lead AI Engineer to help develop advanced AI platform capabilities that will power our enterprise solutions.

About the Role

 As a Lead AI Engineer, you will provide hands-on technical leadership in the design, development, and operation of production AI and Generative AI systems within a regulated, enterprise environment. You will own technical architecture and implementation decisions for AI solutions within a defined business domain, ensuring solutions are scalable, reliable, and compliant with governance and risk standards.

You will work closely with data engineering, platform engineering, DevOps, product, and business stakeholders to translate business requirements into robust AI solutions. This is a lead technical role focused on system delivery and operational excellence, rather than people management or enterprise-wide strategy.

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).
• Platform Integration: Enable seamless integration of AI capabilities into business applications and workflows through APIs, SDKs, and microservices.
• Performance & Scalability: Optimize platform components for efficiency, scalability, and reliability using best practices in distributed computing, resource management, and cloud-native architectures.
• Define and track model performance, drift, bias, and GenAI evaluation metrics to ensure reliability and quality.
• 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.

Core Competencies

• Technical Excellence: Ability to design and lead robust, production-grade AI and GenAI systems using modern engineering and MLOps principles.
• Delivery Focus: Strong orientation toward hands-on implementation while owning end-to-end delivery, quality, and operational readiness of AI solutions.
• Collaboration: Proven ability to work cross-functionally with business, data, platform, risk, and operations teams to deliver compliant AI solutions.
• Innovation: Evaluate and apply emerging AI technologies pragmatically, balancing innovation with reliability, cost, and regulatory constraints.
• Ownership: Accountability for technical decisions, project outcomes, timelines, and production stability within a defined domain.
• Ethics & Governance: Commitment to responsible and ethical AI, including fairness, transparency, explainability, and adherence to regulatory and model risk standards.

Required Skills:

• Bachelor’s or master’s degree in computer science, Data Science, Engineering, Mathematics, or a related field.
• 8+ years of overall experience with 4+ years in AI/ML engineering, data science.
• Proven experience designing, deploying, and operating production ML and GenAI solutions, including APIs, batch, and real-time inference.
• Strong software engineering skills with experience in Python (required).
• Hands-on experience with cloud platforms (AWS, Azure, or GCP).
• Proven experience designing, developing, and deploying Generative AI (GenAI) solutions using large language models (LLMs) such as GPT, Llama, Claude, etc.
• Strong understanding of prompt engineering, SLM fine-tuning, and evaluation of generative models for real-world applications.
• Familiarity with best practices for responsible AI, including data privacy, bias mitigation, and model monitoring.

 Preferred Skills:

• Experience with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and orchestration tools.
• 2+ years in GenAI.
• 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.