AI Platform engineer

Key ResponsibilitiesAI Prompt Engineering & Agent Development

• Design, test, and optimize advanced prompts for LLM-based applications.
• Develop reusable prompt libraries, templates, and orchestration workflows.
• Build Agentic AI solutions leveraging reasoning, tool usage, and workflow automation.
• Implement RAG (Retrieval-Augmented Generation) architectures and knowledge-grounded AI systems.
• Improve accuracy, consistency, explainability, and output quality of AI applications.
• Create evaluation frameworks and benchmarks for prompt effectiveness.

AI Application Engineering

• Develop GenAI-powered enterprise applications and products.
• Integrate Azure OpenAI, OpenAI, Gemini, Claude, and emerging AI models into business workflows.
• Build APIs, microservices, and AI orchestration layers.
• Implement conversational AI assistants, copilots, and workflow automation systems.
• Leverage GitHub Copilot and AI-assisted development methodologies.

Platform Engineering

• Build and manage scalable cloud-native AI platforms on Azure and/or AWS.
• Design CI/CD pipelines supporting AI workloads and model deployment.
• Containerize applications using Docker and Kubernetes.
• Develop Infrastructure-as-Code (Terraform/Bicep/CloudFormation).
• Ensure platform security, observability, governance, and reliability.
• Manage integrations across enterprise systems, data platforms, and AI services.

Data & MLOps Enablement

• Build data pipelines supporting AI applications.
• Manage vector databases and semantic search infrastructure.
• Support model deployment, monitoring, versioning, and lifecycle management.
• Implement AI governance, auditability, and performance tracking.

Product & Delivery Support

• Collaborate with architects, product managers, insurance SMEs, and engineering teams.
• Translate business requirements into AI-enabled solutions.
• Accelerate development productivity through AI-assisted engineering practices.
• Contribute to internal AI accelerators and FLARRE capabilities.