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Mid-level Mumbai role with Kubernetes/AWS and 4+ years raises applicant density despite niche AI-platform focus.
Strong requirements for LLMs, RAG, vector DBs, Kubernetes, and production AI operations reduce cross-industry transferability.
Multiple mandatory technical skills and explicit 4+ years experience enforce strict shortlisting filters.
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Build, maintain, and operate QRT's internal AI application platform focusing on production AI services such as RAG systems, agentic workflows, retrieval infrastructure, and APIs.
Own AI services used firm-wide, delivering scalable, reliable, and high-quality AI capabilities to researchers and engineers.
Define and monitor service performance objectives related to latency, reliability, and retrieval quality; manage service lifecycle including deployment and incident response.
4+ years of software or platform engineering experience with exposure to AI/ML or LLM-based applications.
Strong Kubernetes experience and familiarity with containerized environments.
Proficient in Python, production APIs, AWS, networking fundamentals, IAM, cloud infrastructure, and vector database management.
Hands-on experience building and operating production RAG systems and retrieval systems; understanding of LLM fundamentals including prompting and context management.
Experience integrating and building production-grade AI services and APIs supporting LLM-powered workflows and agentic systems.
Proven ability to implement observability, prompt versioning, testing frameworks, and resilient degradation mechanisms for complex AI platforms.
Skilled collaborator capable of interfacing with both technical and non-technical teams to deliver scalable AI platform solutions across a quantitative research environment.