Please mention DailyRemote when applying
AI Summary
The AI Engineer will design and build Go services that integrate LLMs into production workflows, ensuring structured output and reliable workflows. They will also define evaluation metrics for AI components and ship multi-tenant services on Google Cloud.
This is a remote position.
Role: AI Engineer
Experience: 4–8 years
We are looking for a Senior AI Engineer who treats LLMs as an engineering substrate — someone who
builds production-grade Go services on Google Cloud that turn model output into structured, deterministic,
schema-valid data the rest of the system can trust. This is a hands-on individual-contributor role with
significant ownership over design and implementation.
Our AI/ML work spans several modules — some LLM-backed, some deterministic — and you may contribute
across them over time. We therefore value engineers who are adaptable and strong on fundamentals over
narrow specialists, and who can pick up a new problem space quickly.
Key Responsibilities
• Build & integrate LLMs: Design and build Go services that integrate LLMs into production workflows —
with strict structured output, confidence handling, and deterministic fallbacks when the model is
unavailable or low-confidence.
• Reliable agentic workflows: Build multi-step and agentic workflows that execute reasoning, handle
errors, and maintain state — treating retries, timeouts, rate limiting, and graceful degradation as firstclass concerns.
• Structured & deterministic output: Enforce strict Data systems: Work across the team's data and messaging stack — graph, analytical, and event-driven
stores — modeling data and writing efficient queries.
• Evaluation & reliability: Define and own evaluation for AI components — datasets, regression/eval
harnesses, and metrics for accuracy, latency, cost, and reliability — so prompt and model changes ship
safely.
• Production engineering: Ship multi-tenant, observable services on GCP that meet the team's coding
standard, and review peers' work to the same bar.Mandatory Skills & Qualifications
• Go (Golang), production-grade: Strong, idiomatic Go — concurrency (goroutines, channels, context),
disciplined error handling, and clean, testable service code. You have shipped and maintained Go
backend services in production.
• Applied LLM engineering: Hands-on experience integrating LLMs into production systems — prompt
design, structured/JSON output, function/tool calling, confidence handling, and fallback strategies. A
framework-agnostic grasp of agentic patterns (tool use, multi-step reasoning, state) and why reliability
matters more than cleverness.
• GCP & Vertex AI: Practical experience on Google Cloud, ideally with Vertex AI (Gemini) and common data
and eventing services.
• System-engineering mindset: You approach AI as an engineering problem — idempotency, retries, rate
limiting, timeouts, structured I/O, and graceful degradation rather than just prompt tuning. You design
for observabiData systems: Comfortable with SQL and at least one of: analytical (e.g. BigQuery), graph (e.g. Neo4j /
Cypher), or relational (e.g. PostgreSQL) stores. You can model data and write efficient queries.
• APIs & services: You build clean service interfaces (gRPC / REST / GraphQL) and understand how to
expose backend logic as well-bounded “tools” that AI components can call safely.
Optional (But Highly Valued) Skills
• Python: For prototyping, evaluation tooling, data work, or ML experimentation alongside the primary Go
stack.
• Agent orchestration frameworks: Experience with agent / LLM-orchestration frameworks (e.g. Firebase
Genkit) or comparable tooling.
• Knowledge graphs: Graph modeling, GraphRAG, or relationship inference at scale on graph databases.
• Time-series & ML: Forecasting (e.g. ARIMA and related methods), BigQuery ML, or applied model
evaluation.
• LLM security: Awareness of the OWASP Top 10 for LLM Applications (prompt/query injection, insecure
output handling, excessive agency), particularly where model output drives queries or actions.
• Containerization & delivery: Docker, Kubernetes (GKE), and CI/CD.
Cost & latency optimization: Caching, batching, and model-tier selection to keep AI workloads efficient
at scale.
Tech Stack & Standards
(Experience in these or similar technologies is preferred)
• Language: Go (primary); Python a plus.
• AI / LLM: Vertex AI Gemini; agent-orchestration frameworks (e.g. Firebase Genkit).
• Data & messaging: Graph (e.g. Neo4j / Cypher), analytical (e.g. BigQuery / BigQuery ML), object storage
and event streaming (e.g. Cloud Storage, Pub/Sub).
• Cloud & deployment: Google Cloud Platform; containers and Kubernetes (GKE).
Engineering standards: Multi-tenant isolation, structured error handling, and automated evaluation for
AI components.
• Observability: New Relic or equivalent.
Automatically Apply to the Best Remote Jobs
Stop the endless job search. Our AI finds and applies to the best jobs for you.
