AI Engineer

Role: AI Platform Engineer – GenAI / LLM Infrastructure Location: Hyderabad Experience: 1–4 years Job Description: 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

1. 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)

2. 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

1. 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

2. 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

1. 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

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

2. 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

Qualifications: Core Engineering Strong Python programming

• Experience with: Backend systems / APIs Data pipelines (ETL / processing frameworks)

• AI Platform & MLOps Understanding of: ML lifecycle management CI/CD pipelines Model deployment strategies

• Exposure to: LLM ecosystems (OpenAI / Azure / HuggingFace) API-based AI integration

• Systems & Infrastructure Knowledge of: Distributed systems concepts System design fundamentals Familiarity with: Containerization (Docker) Orchestration tools (Kubernetes)

• Good to Have Skills Experience with: Vector databases (Pinecone, FAISS) Workflow orchestration tools

• Exposure to Cloud platforms (Azure / AWS / GCP) Understanding of Observability tools (monitoring/logging systems)

• Domain Expertise (Preferred) Exposure to enterprise systems in: Insurance / BFSI domain Understanding of: Data security and compliance requirements Large-scale enterprise architecture