Overview
Embed directly with customer teams as the on-the-ground engineer, owning an outcome and shipping a production-grade SpaceXSI system that keeps running after you leave, while serving as the critical technical feedback loop from the field back to product engineering.
What you'll do
- Lead discovery with the customer: orient in their codebases and workflows, find the real bottleneck and the use case worth building, and define clear success metrics
- Design and ship production agents, AI applications, and coding-agent workflows on Grok and our models, on live business processes and inside the customer's engineering org
- Get a first version live in days, then harden it over weeks with rollout, monitoring, and iteration from real usage
- Own production quality: tracing, evals, debugging model or delivery failures, and latency and cost tradeoffs
- Build the systems around the model (tools, MCP servers, retrieval, rules, skills, CI gates, and evals) and integrate them into the customer's auth, data, workflow, and delivery systems; decide when prompting, architecture, data, process, or a model change is the right lever
- Measure the outcome that matters (revenue, cost, hours saved, error rate, cycle time, escaped defects), not seat adoption
- Work directly with Staff+, platform, and domain leaders, going deep in their systems while communicating clearly about tradeoffs and results
- Leave the customer team able to run what you built, and turn what you learn into reusable patterns and improvements to SpaceXSI products
What you'll need
- 5+ years of experience in software engineering, machine learning engineering, or data science
- Have owned a customer or operator outcome, not just a slide or a prototype, and can turn a fuzzy problem into a scoped, shipped system
- Built and owned AI-native workflows or agents in production (using Python, JavaScript/TypeScript), and debugged real production failures (model, tool, data, pipeline, or delivery path)
Nice to have
- 2+ years in a customer-facing role, leading discovery conversations and being accountable for outcomes for external stakeholders
- Hands-on depth in several of: agents and tool-calling, eval harnesses, retrieval and context systems, CI/CD or developer platforms, and AI-native coding workflows in a real repo
- Experience as a forward-deployed, solutions, or platform engineer embedded with customers, or as a founding engineer who owned an AI product end to end
Details
- Location: Bengaluru, India
Read the full description and apply on the company’s own careers page.