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

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)
  • Support “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

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