Applied AI Engineer
The Security Resiliency Organization is rebuilding fragmented, manually-run security processes into consolidated, AI-native workflows that run in production at scale. As the most senior IC on the applied AI team, you will set the engineering standard the rest of the team builds against and own the hardest implementation problems.
Join us as an Applied AI Engineer on our Security Resiliency team in Bangalore to do the best work of your career and make a profound social impact.
What you'll achieve
You will set the technical bar for how AI-native security workflows are built, instrumented and hardened for production.
You will own consolidation of duplicated processes into single, reusable implementations that every dependent function shares.
You will establish the reusable agent patterns, context strategy and integration surfaces that every later workflow builds on.
You will
Architect agentic implementations using LangGraph (or equivalent orchestration) – sub-agent delegation, state and checkpoint handling, and deterministic gating and verdict logic – from approved solution designs.
Own context engineering end to end: retrieval and search, memory, embedding-based grounding against enterprise data, prompt assembly and schema-constrained output under real payload sizes.
Instrument production systems with LangFuse (or equivalent) for tracing, token/cost accounting and latency measurement, deploying on Kubernetes and Docker with PostgreSQL and Elasticsearch as the data and retrieval layers.
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Work directly with functional owners to establish operational detail a design does not carry, and re-engineer the process itself rather than reproducing it step for step.
Resolve production defects escalated from agent operations at root cause, and raise design gaps to the solution owner rather than resolving them unilaterally in code.
Take the first step towards your dream career
Every Dell Technologies team member brings something unique to the table. Here’s what we are looking for with this role:
Essential Requirements
12+ years in software engineering with direct ownership of systems in production; agentic systems personally built and shipped, not prototypes or proofs of concept.
Strong Python plus one of TypeScript/JavaScript or Go, with deep code-quality practice (testing, review, refactoring) at scale.
Deep hands-on experience with LangGraph (or equivalent), embedding concepts, retrieval-augmented generation and LLM observability tooling such as LangFuse.
Production-grade command of Kubernetes, Docker, PostgreSQL and Elasticsearch, plus REST/GraphQL APIs, OAuth and scoped credential handling.
Process re-engineering experience: rebuilding a manual or fragmented workflow around what agentic automation makes possible, working directly with the people who perform it today.
Desirable Requirements
Experience building MCP servers or agent tooling, and working knowledge of agentic orchestration frameworks beyond LangGraph, with judgment on when to use them versus build custom.
LLMOps in production and security-domain AI experience (application security, DevSecOps, vulnerability management, software supply-chain security).
