Requirements

• 4+ years of experience in software or platform engineering, with exposure to AI/ML or LLM-based applications
• Strong Kubernetes experience and familiarity with containerised environments
• Good knowledge of AWS, networking fundamentals, IAM, and cloud infrastructure
• Hands-on experience building and operating production RAG systems
• Experience with vector databases and retrieval systems
• Strong Python skills and experience building production APIs and services
• Understanding of LLM fundamentals, including prompting, context management, token constraints, and output reliability
• Strong communication skills and the ability to collaborate across technical and non-technical teams

Nice to Have

• Experience with agentic AI systems and workflow orchestration
• Familiarity with LLM evaluation frameworks and quality measurement
• Exposure to model serving platforms and inference optimization
• Understanding of embedding model trade-offs and retrieval performance
• Experience with data engineering or AI-related data pipelines
• AWS or Kubernetes certifications

What You'll Be Doing

• Develop and maintain internal AI services and APIs
• Build and improve RAG pipelines, including document ingestion, embeddings, retrieval, and relevance optimization
• Manage vector database performance, scalability, and data freshness
• Design clear, well-documented APIs for internal users
• Support agentic workflows and the services they depend on
• Integrate model serving endpoints into application-layer services
• Define and monitor service objectives around latency, reliability, and retrieval quality
• Implement prompt management, versioning, evaluation, and testing frameworks
• Build resilient systems with fallback and degradation mechanisms
• Implement monitoring, tracing, logging, and quality metrics across AI services
• Manage service lifecycle activities, including deployment, rollout, versioning, and deprecation
• Participate in operational support and incident response

Perks and Benefits

• Equal opportunity employer promoting diversity for success
• Empower employees to work openly and respectfully for collective success
• Initiatives and programs for achieving a healthy work-life balance