AI Engineer

Job Information

Job Code

VL/IN/CMD/CI4/2026/355

Work Experience

5 - 7 Years

Date Opened

02/10/2026

Industry

IT Services

Job Type

Full Time Employment

Remote Job

Job Description

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 first￾class 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.