Lead Data and AI Solution Engineer

The Great Eastern Life Assurance Company Limited; Santo Niño 1st; On-site; PHP 1,200,000 - 1,800,000; Full time

Job summary

The Great Eastern Life Assurance Company Limited seeks a seasoned GenAI/ML Engineer to design and deliver advanced AI solutions, architect GenAI platforms, and deploy models on AWS. You will collaborate with data scientists and engineers to build scalable data pipelines and integrate AI into client workflows.

Responsibilities include end-to-end GenAI app development, API/microservice design, and robust DevOps practices to ensure reliable production systems across cloud ecosystems.

Qualifications

• Requirement Analysis & Solution Design
• Collaborate with stakeholders to translate requirements into technical specifications and architect GenAI solutions.
• Develop and deploy generative AI models (e.g., LLMs, GANs) on AWS.
• Deploy scalable production-grade AI solutions with reliability.
• Cross-functional collaboration with data scientists and software engineers.
• Data pipeline design, preprocessing, cleaning, and augmentation.
• System integration & API development for real-time AI functionality.
• End-to-End GenAI app development including frontend (React) and backend services.

Responsibilities

• Implement and deliver advanced data and AI solutions driving innovation and efficiency.
• Analyze requirements and design GenAI architectures in collaboration with stakeholders.
• Develop and deploy GenAI models on AWS and ensure scalable production readiness.
• Create and maintain robust data pipelines for preprocessing and enrichment.
• Integrate GenAI capabilities into client infrastructure via APIs and microservices.
• Develop end-to-end GenAI apps with frontend and backend integration.

Skills

GenAI engineering

Full-stack development

Backend development

AI model deployment

Data pipelines

Cloud platforms

MLOps

API development

React frontend

Tools

AWS

Azure

GCP

Terraform

CloudFormation

Docker

Kubernetes

Apache Spark

Airflow

Databricks

SageMaker

Vertex AI

Azure ML

Job description

• Implement and deliver advanced data and AI solutions that drive innovation, optimize decision-making, and enhance operational efficiency—contributing directly to the organization's strategic growth and technological leadership.
• Requirement Analysis & Solution Design
• Collaborate with stakeholders to understand business needs, translate requirements into technical specifications, and architect tailored GenAI solutions (e.g., chatbots, content generators) that align with project goals.
• Development & Deployment of GenAI Models Develop and deploy generative AI models (e.g., LLMs, GANs) on AWS.
• Deploy scalable solutions into production environments, ensuring robustness and reliability.
• Cross-functional Collaboration Partner with data scientists, software engineers, and business teams to integrate AI capabilities into workflows, ensuring alignment with technical and operational objectives.
• Data Pipeline Management
• Design and maintain efficient data pipelines for preprocessing, cleaning, and augmenting datasets.
• Ensure data quality, governance, and compliance with privacy regulations System Integration & API Development Seamlessly integrate GenAI solutions into existing client infrastructure (e.g., cloud platforms, enterprise systems). Develop APIs and microservices to enable real-time AI functionality.
• End-to-End GenAI Application Development Design, develop, and deploy user-facing applications powered by GenAI, including frontend interfaces (e.g., React) and backend services. Integrate AI models into applications to deliver seamless

• AI/ML Engineering: 3–5 years of hands-on experience in designing, developing, and deploying machine learning/AI solutions, with 1–2 years focused on generative AI (e.g., LLMs, GANs, diffusion models).
• Application Development: Proven track record in full-stack or backend development, including building and deploying AI-powered applications (e.g., chatbots, content automation tools).
• Cloud Platforms: Experience with cloud services (AWS, Azure, GCP) for AI/ML workflows (e.g., SageMaker, Vertex AI, Azure ML) and infrastructure-as-code (Terraform, CloudFormation). Data Engineering: Expertise in building scalable data pipelines (ETL/ELT) using tools like Apache Spark, Airflow, or Databricks.
• DevOps/MLOps: Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes), and model monitoring tools (MLflow).