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AI/ML Engineering
Machine Learning Operations Engineer
Principal
Bulgaria, Croatia, Egypt, Poland, Portugal, Spain
Remote
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Over 20 years of market experience, Intellias brings together technologists, creators and innovators in Europe, North and Latin America, and the Middle East. Join our international team and take the mission to solve the advanced tech challenges of tomorrow!
What project we have for you
We are seeking a highly experienced and hands-on Principal AI/ML Architect & Applied AI Lead to drive the design, development, and operationalization of enterprise-scale AI systems across research and production environments.
This role combines deep technical expertise in Machine Learning, Generative AI, distributed data systems, and cloud-native architectures with strategic leadership capabilities. The ideal candidate will lead complex AI initiatives end-to-end — from experimentation and research to scalable deployment in global enterprise environments.
The position requires a strong balance between:
- technical leadership,
- hands-on implementation,
- AI strategy,
- cross-functional collaboration,
- and mentoring of engineering and data science teams.
What you will do
- Lead the design and implementation of AI/ML solutions across multiple business domains.
- Drive enterprise adoption of Large Language Models (LLMs), Generative AI, NLP/NLU, and advanced analytics solutions.
- Define AI architecture standards, MLOps best practices, and scalable deployment strategies.
- Evaluate emerging AI technologies and identify opportunities for innovation and operational impact.
- Architect scalable distributed data-processing systems capable of handling large-scale datasets and real-time pipelines.
- Model-portable design: gateway-based LLM access (e.g. Bedrock, LLM-gateway/router products), designing so models can be swapped without re-architecture. This is a live workstream on the account.
– Platform capability migration: experience consolidating/migrating AI
capabilities from one platform onto another (strangler-style,
incremental), not greenfield-only.
– Architecture governance: design decision records, keeping design docs
and code in sync, documenting so knowledge survives team rotation. - Integrate an AI platform with an enterprise lakehouse (Databricks/Unity Catalog): design the interface to the semantic layer, reconcile access-control models (UC row-level security vs application-layer scoping), plan migration sequencin
- Lead cloud migration and modernisation initiatives from platform to platform
- Ensure reliability, scalability, observability, and cost-efficiency of AI infrastructure.
- Design and implement enterprise-grade chatbot and conversational AI platforms.
- Lead development of Retrieval-Augmented Generation (RAG), agentic workflows, and LLM orchestration systems.
- Define governance, evaluation, and monitoring strategies for GenAI systems.
- Lead cross-functional teams composed of data scientists, ML engineers, software engineers, and business stakeholders.
- Mentor engineers in AI/ML best practices, architecture, and software engineering standards.
- Coordinate global AI initiatives across distributed teams and multiple geographies.
- Communicate technical concepts effectively to executive and non-technical audiences.
- Support innovation programs and AI adoption strategies across the organization.
What you need for this
- Hands-on architecture of production LLM/agentic systems: agent runtimes, agentic RAG, tool/function-calling integration, multi-agent orchestration. Cloud agent platform experience (AWS Bedrock/AgentCore or equivalent).
- LLM evaluation and observability in production: eval pipelines, regression gates, tracing, dashboards (not just offline notebooks).
- Security and access-control literacy for AI platforms: enterprise identity (Entra/OAuth), role/group-based data access, permission-aware retrieval, auditability.
- Working fluency with AI coding agents (Claude Code / Copilot-class) in own workflow, and ability to mentor a team into agent-native ways of working.
- Consulting posture: embedded advisory work with client stakeholders, able to align and decide with executives, not only build.
- English B2/C1 (daily EU + US/Canada stakeholder communication), proven multinational experience.
- 5+ years of experience in AI/ML, data science, or distributed systems engineering.
- Proven experience designing and deploying production-grade AI solutions at enterprise scale.
- Experience leading global or distributed technical teams.
- Demonstrated success delivering AI transformation initiatives
- SQL / NoSQL databases
- Docker
- Kubernetes
- CI/CD pipelines
- Infrastructure-as-Code
- MLOps frameworks
- Python
What it’s like to work at Intellias
At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth.
We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law.
We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.
Skills
artificial_intelligence
