Build and ship LLM-powered systems that reduce toil, accelerate remediation, and improve decision-making in operations contexts.
Design and maintain evaluation frameworks: hallucination tests, regression harnesses, benchmarks, and quality gates for safe rollout.
Develop retrieval-augmented pipelines (RAG) and data strategies for grounding on logs, telemetry, runbooks, and system metadata.
Engineer AI copilots and natural-language interfaces to interact with operational data and workflows.
Create frameworks for large-scale automation such as safe code migration and transformation pipelines.
Apply adaptive AI techniques to optimize system configurations, predict anomalies, and recommend preventive actions.
Partner across teams — collaborate with AI Platform (inference/serving), SRE/Infra/Data Service/DC (operational context), and Security (safe usage) while focusing your work on application logic, correctness, evaluation, and safety.
Implement guardrails and safety systems: prompt injection defenses, PII filtering, constrained decoding, and model observability.
Build developer-facing SDKs and APIs in Python/Go; intuitive UIs in JavaScript/React for human-in-the-loop workflows.
Leverage modern orchestration frameworks (LangChain, LangGraph, MCP, semantic routers) to coordinate multi-step, tool-augmented workflows.

A Bachelor’s Degree (or higher degree) in an engineering or Technology field (Computer Science, Computer Engineering, Applied Mathematics, analytics, Technology in any applied discipline..)
Proven experience shipping LLM-based systems into production with measurable impact.
Expertise in evaluation and testing of LLMs (benchmarks, hallucination/regression tests, grounding metrics).
Strong programming skills in Python and Go.
Hands-on experience with LLM orchestration frameworks: LangChain, LangGraph, MCP, agent frameworks, or equivalent.
Deep understanding of RAG pipelines: embeddings, retrieval quality metrics, re-ranking, and grounding precision/recall.
Ability to translate ambiguous operational problems into AI-first solutions with clear KPIs.
Bonus Points: Experience with fine-tuning/adapters (LoRA, QLoRA, continual learning) and safety tuning.
Exposure to inference optimization and serving, partnering with platform teams on latency, scaling, and resilience.
Experience building AI copilots/assistants for engineers.
Frontend development in JavaScript/React for lightweight human-in-the-loop tooling.
Knowledge of reinforcement learning, adaptive systems, or optimization methods.

Market leader in compensation and equity awards
Comprehensive physical and mental wellness programs
Competitive vacation and holidays for recharge
Paid parental and adoption leaves
Professional development opportunities for all employees regardless of level or role
Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
Vibrant office culture with world class amenities
Great Place to Work Certified™ across the globe

Senior Software Engineer – Applied AI

Key skills