We’re building real, production‑grade AI that helps people work smarter and creates meaningful impact. As an AI Engineer, you’ll design, ship, and operate LLM‑powered solutions agents, RAG/CAG pipelines, and intelligent services using modern engineering practices. If you’re a curious builder who loves to learn, prototype, and turn ideas into durable products, you’ll feel at home here.

What you’ll do :

• Design & build LLM applications end‑to‑end (APIs, services, and/or simple UIs) that solve concrete user problems.

• Implement retrieval‑augmented (RAG/CAG) and tool‑augmented patterns, including chunking/embedding strategies, vector stores, reranking, and prompt orchestration.

• Develop agentic workflows (tool use/function calling, planning/critique loops, MCP‑based integrations when relevant) with robust guardrails and observability.

• Own LLMOps/MLOps fundamentals: offline/online evaluation, A/B testing, data & prompt versioning, red‑teaming/safety checks, latency/cost monitoring, and drift detection.

• Engineer reliable backends in Python and JavaScript/TypeScript (Node), with Go as a strong plus for high‑performance services.

• Ship to production with CI/CD, containers (Docker), and cloud services (e.g., Azure/AWS/GCP), including secrets, logging, and runtime monitoring.

• Partner cross‑functionally with PMs, designers, data engineers, security/privacy, and domain experts to refine requirements and measure business impact.

• Continuously learn: explore new models, patterns, and tools; share findings via demos, brown‑bags, or docs to uplift the team.

What you’ll bring (Must‑have) :

• Programming languages: Strong proficiency in Python and JavaScript/TypeScript; production experience building APIs/services. Go is a big plus.

• LLM foundations: Working knowledge of LLMs and modern patterns like RAG, CAG, MCP; prompt engineering and function/tool calling.

• Data & retrieval: Experience with embeddings/vector databases, document chunking, metadata, and retrieval/reranking strategies.

• Software engineering: Solid grasp of testing, code quality, design patterns, API design (REST/gRPC), and performance troubleshooting.

• Cloud & DevOps: Practical experience with at least one major cloud (Azure/AWS/GCP), containers, CI/CD, and infrastructure basics.

• Security & responsibility: Awareness of privacy, safety, and responsible‑AI guardrails when handling sensitive data and user interactions.

• Mindset: Curious, hands‑on “geek” builder who iterates quickly, learns continuously, and enjoys sharing knowledge.

Nice to have :

• Go for high‑throughput microservices or systems work.

• Agent frameworks and orchestration tools (LangChain, LlamaIndex, MCP servers/clients, workflows/queues).

• Evaluation tooling (prompt/test harnesses, deterministic fixtures, LLM judges, hallucination/grounding checks).

• Search & data (BM25/sparse retrieval, hybrid search, knowledge graphs).

• Front‑end basics (React/Next.js) to prototype end‑to‑end experiences.

• Observability (OpenTelemetry, tracing, metrics, cost dashboards).

• Regulatory awareness (e.g., EU AI Act considerations) and security best practices (PII handling, RBAC, key management).

• Multilingual environments and domain experience in Technology.

Our stack :

• Backend: Python (FastAPI), Node/TypeScript (Express/Next.js), Go

• AI/LLM: Embeddings & vector DBs, rerankers, function/tool calling, orchestration workflows

• Data/Infra: Cloud (Azure/AWS/GCP), Docker/K8s, PostgreSQL, Redis, message queues

• Observability: Logs/metrics/traces, cost & latency dashboards, evaluation pipelines

• Collab: Git, CI/CD, feature flags, experiment tracking

Who you are :

• A learner‑enthusiast who reads, prototypes, and shares.

• A pragmatic engineer who loves clean code and measurable outcomes.

• A mission‑driven teammate who wants tech to make the world better for users and colleagues.