AI Engineer

Role Summary We are hiring AI Platform Engineers to design, build, and scale the core AI infrastructure and platform capabilities that power enterprise-grade AI solutions. This role focuses on:

• • Building reusable AI infrastructure and pipelines
  • Enabling scalable deployment of LLM-based systems
  • Ensuring reliability, observability, and governance of AI systems

Key Responsibilities: 🔹 1. AI/ML Infrastructure & Pipeline Engineering - Design and build end-to-end AI/ML pipelines, including: - Data ingestion - Data transformation - Model interaction (LLMs / APIs) - Output processing - Implement scalable and modular pipelines for enterprise AI systems

🔹 2. LLM Platform & System Design - Build foundational components for LLM-based systems, including: - Prompt orchestration frameworks - Retrieval pipelines (RAG infrastructure) - Context management layers - Enable standardized patterns for integrating multiple LLM providers (OpenAI, Azure, HuggingFace)

🔹 3. MLOps & AI Deployment - Develop and maintain CI/CD pipelines for AI systems - Enable: - Automated deployment of AI models - Version control for models and workflows - Continuous evaluation and monitoring - Build systems for: - Experiment tracking - Model lifecycle management

🔹 4. System Reliability, Scalability & Performance - Ensure AI systems meet enterprise-grade requirements for: - Scalability - High availability - Low latency - Optimize infrastructure for: - Cost efficiency - Performance of LLM-based workloads - Implement failover, retry, and resilience mechanisms

🔹 5. Observability & Governance - Design and implement monitoring systems for: - Model performance - Drift detection - Latency and usage metrics - Build guardrails for: - Responsible AI usage - Output validation and traceability - Ensure compliance with enterprise-grade governance requirements

🔹 6. Reusable Platform Components - Build reusable platform modules such as: - AI service layers - Model serving endpoints - Workflow orchestration frameworks - Enable internal teams to build AI applications on top of standardized platform capabilities

🔹 7. Integration with Enterprise Ecosystems - Enable AI systems to integrate seamlessly with: - Enterprise applications - Insurance platforms (e.g., Duck Creek ecosystem) - "no data leaves environment" principles and secure deployment architectures

🔹 8. Collaboration & Platform Enablement - Work closely with: - AI Application Engineers - Product Managers (Flarre) - DevOps and Cloud teams - Enable broader engineering teams to build and deploy AI solutions on the platform

• Strong Python programming
  • Experience with:
    • Backend systems / APIs
    • Data pipelines (ETL / processing frameworks)
  • Understanding of:
    • ML lifecycle management
    • CI/CD pipelines
    • Model deployment strategies
  • Exposure to:
    • LLM ecosystems (OpenAI / Azure / HuggingFace)
    • API-based AI integration
  • Knowledge of:
    • Distributed systems concepts
    • System design fundamentals
  • Familiarity with:
    • Containerization (Docker)
    • Orchestration tools (Kubernetes)
  • Experience with:
    • Vector databases (Pinecone, FAISS)
    • Workflow orchestration tools
  • Exposure to Cloud platforms (Azure / AWS / GCP)
  • Understanding of Observability tools (monitoring/logging systems)