About the role
- 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
- Design and build end-to-end AI/ML pipelines, including:
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
- Ensure AI systems meet enterprise-grade requirements for:
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
- Design and implement monitoring systems for:
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
- Build reusable platform modules such as:
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
- Work closely with:
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
- Understanding of:
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)
- Experience with:
Domain Expertise (Preferred)
- Exposure to enterprise systems in:
- Insurance / BFSI domain
- Understanding of:
- Data security and compliance requirements
- Large-scale enterprise architecture