MetLife

MGCC GG 11.2– Senior AI Engineer II

Position Title:

MGCC GG 11.2– Senior AI Engineer II

Function, Responsibility Level: 

Reports to (Responsibility Level): GG 12 or above 

Supervises: 

GG 10.1 – AI Engineer I

GG 10.2 - AI Engineer II

GG 11.1 - Senior AI 

Location: Hyderabad

Global Grade: 11

Cost Center (85 series): 

Complexity: OSTG

PID/s Load Mapping:

Position Summary

 MetLife established a Global capability center (MGCC) in India to scale and mature Data & Analytics, technology and operations capabilities in a cost-effective manner and make MetLife future ready. The center is integral to Global Technology and Operations with a focus to protect & build MetLife IP, promote reusability and drive experimentation and innovation. The Data & Analytics team in India mirrors the Global D&A team with an objective to drive business value through trusted data, scaled capabilities, and actionable insights. The operating models consists of business aligned data officers- US, Japan and LatAm & Corporate functions enabled by enterprise COEs- data engineering, data governance and data science

Role Value Proposition:  

AI Engineer plays a critical role in data and analytics life cycle and significantly contributes to production grade data and analytics solutions. The role requires one to demonstrate architecting, designing, and implementing the core AI and machine learning (ML) infrastructure that underpins our next-generation business applications. Besides contributing in individual capacity to solve wide ranging business problems, this role also leads and develops Data and Analytics talent

Job Responsibilities

• Design , 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.
• 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)
• Develop and maintain CI/CD pipelines for ML models, ensuring automated testing, versioning, deployment, and monitoring in production environments.
• Documentation & Knowledge Sharing: Produce clear technical documentation and participate in knowledge sharing to mentor team members and enable  
• Independently lead design, solutioning & estimations• Provide people leadership: coach, develop and engage talent

Knowledge, Skills and Abilities

Education

Bachelor’s degree in computer science, information technology or equivalent educational qualification

Technical Skills and Experience

• 10-12+ years of relevant experience

• 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• People leadership: talent development & engagement experience

Good to Have (Preferred)

• Azure ML or cloud AI/ML Services
• 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.
• Advocate for security, privacy, ethical, and compliance best practices throughout the AI/ML platform and model development lifecycle.
• Optimize platform components for efficiency, scalability, and reliability using best practices in distributed computing, resource management, and cloud-native architectures.