About this role
This role sits on the AI applications team at the enterprise storage company, and it is platform engineering rather than model work. The framing is useful: you own the inner loop of AI development, making sure LLM applications, RAG pipelines and agentic workflows are built, tested and deployed with the same rigour as ordinary software. Concretely, that means internal APIs so engineers can provision AI ready environments with model weights, vector databases and event streams in one command, Go and Python tooling that automates root cause analysis of infrastructure failures, Kafka or RabbitMQ pipelines for long running asynchronous AI work, and Kubernetes with GPU workloads and vector database storage. The posting is explicit that it wants a code first infrastructure mindset, replacing brittle shell scripts with type safe Go tooling.
Who this is for
Asks for 8+ years in platform infrastructure engineering with a shift towards AI application development. Required: strong proficiency in Python for AI logic and Go for platform tools; practical experience with retrieval augmented generation, prompt engineering and integrating LLM APIs, with OpenAI, Anthropic and local models via Ollama named; the ability to design and implement high performance Go services listening to Kafka or RabbitMQ streams to trigger dynamic infrastructure scaling; experience managing and optimising infrastructure for vector databases and distributed caches such as Redis for high availability RAG data; the ability to replace shell scripts with robust, type safe internal tooling in Go for environment provisioning and disaster recovery; deep Kubernetes experience specifically managing GPU workloads and specialised storage for vector databases; proven experience with Kafka or RabbitMQ for high volume streams; and observability using Prometheus and Grafana for both system health and AI specific metrics such as token latency and model drift.
The posting also asks for expertise in high concurrency systems, specifically using Go goroutines and channels to handle large numbers of concurrent operations, and a code first mindset that treats infrastructure problems as software problems.
Day to day: create internal APIs and abstractions for self service provisioning of AI ready environments; build internal tools and AI agents in Go and Python that automate root cause analysis and optimise infrastructure; build asynchronous AI processing pipelines such as long running document ingestion for RAG; and ensure reproducible golden path deployments with Docker and Kubernetes so model, prompt and code stay synchronised across environments.
Location is Bangalore, and this employer states in its postings that it is primarily an in office environment.
No interview process is published.
The posting also asks for expertise in high concurrency systems, specifically using Go goroutines and channels to handle large numbers of concurrent operations, and a code first mindset that treats infrastructure problems as software problems.
Day to day: create internal APIs and abstractions for self service provisioning of AI ready environments; build internal tools and AI agents in Go and Python that automate root cause analysis and optimise infrastructure; build asynchronous AI processing pipelines such as long running document ingestion for RAG; and ensure reproducible golden path deployments with Docker and Kubernetes so model, prompt and code stay synchronised across environments.
Location is Bangalore, and this employer states in its postings that it is primarily an in office environment.
No interview process is published.
Apply on company site
Opens job-boards.greenhouse.io, the employer's own application page. Applying is always free.