We’re looking for a hands‑on AI Engineer who can design, build, and operationalize production‑grade AI systems–while also acting as a trusted technical advisor to clients and a partner to our CTO. This is a role with real impact.
You will influence how we architect, standardize, and deliver GenAI solutions across engagements: from discovery and solution design, through implementation, to production hardening and continuous improvement.
You’ll combine strong engineering fundamentals with consulting skills: framing problems, assessing feasibility, leading technical discussions, and making architectural trade‑offs that stand up to enterprise scrutiny.
Responsibilities
Build & Ship
Develop and deploy enterprise‑grade GenAI applications: conversational search, RAG systems, multimodal agents, and domain‑specific classification services.
Build robust data and language‑processing pipelines using Python, LangChain, and cloud‑native components.
Implement retrieval architectures using Vertex AI Search, Vector Search, or other vector database solutions.
Optimize GenAI solutions for quality, reliability, latency, and cost—including RAG tuning and targeted fine‑tuning where justified by business value.
Production Readiness
Define and implement evaluation frameworks: automated tests, regression checks, hallucination/faithfulness indicators.
Set up monitoring for model performance, app reliability, and business‑aligned KPIs.
Establish best practices around deployment, versioning, observability, incident reviews, and repeatable delivery patterns.
Advisory & Client‑Facing Work
Translate business goals into viable, scalable GenAI architectures on Google Cloud – with clear assumptions, risks, and acceptance criteria.
Lead or co‑lead discovery and feasibility workshops, focusing on use case framing and data readiness within the GCP ecosystem .
Support presales: providing solution options, delivery approaches, and realistic implementation plans.
What we are looking for
2‑5+ years of hands‑on AI/ML development experience, including LLMs and NLP.
Strong Python engineering skills; practical experience with LangChain, Streamlit, and ML frameworks.
Solid understanding of RAG architectures and LLM fine‑tuning.
Familiarity with the end‑to‑end AI/ML lifecycle, evaluation methods, efficiency metrics, and deployment patterns.
Ability to communicate clearly with both technical and non‑technical stakeholders.
Proficiency in English .
Nice to have
Experience delivering AI solutions in consulting environments.
Awareness of GenAI‑related security considerations: PII, access control, prompt injection, data residency.
Cloud‑native deployment experience (containers, CI/CD, infra patterns) within GCP .
What we offer
We work with global enterprise clients on high‑impact data & AI initiatives - which means real architectural challenges, technical ownership, and room to grow.
Remote‑first, flexible working model
Projects involving building modern data & AI platforms from scratch
Full working equipment
Regular knowledge‑sharing sessions and mentoring
Collaboration with international clients (Switzerland, France, UK, US, UAE, and more)
A real team culture built on trust, autonomy, and high standards
Team integrations, without the awkward corporate vibe
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