Senior Full-Stack AI ML Engineer

Direct Mission & Ownership Note

This role carries full End-to-End ownership: you are responsible for the entire project lifecycle - from identifying the business problem and engineering the data to deploying and maintaining the model in production.

1. Work Model: Remote (work from anywhere in EU).
2. Strategic Travel: Requires a one-week trip to the USA once per quarter to collaborate on-site and understand physical operations (all travel costs fully covered).
3. Collaboration Model: Strictly B2B contract.
4. Seniority: This level of autonomy requires a minimum of 7+ years of hands-on experience. We need a veteran who doesn't need a roadmap provided for them - they build it.

About the Role

We are seeking a Senior Full-Stack AI ML Engineer to design, build, deploy, and operate production-grade machine learning systems for manufacturing and operational use cases. This role is explicitly focused on classical and applied machine learning, not LLMcentric or prompt-engineering-only work. The successful candidate will demonstrate deep hands-on experience across the full ML lifecycle: from data engineering and statistical modeling to deployment, monitoring, and continuous improvement in production.

You will work in ambiguous, real-world problem spaces, translating operational and business challenges into measurable ML solutions. The role requires strong engineering fundamentals, applied ML expertise, and the ability to own systems end-to-end in a cloud environment. The ideal candidate combines deep technical skills with a consulting mindset and can occasionally act as a solution owner / product lead.

Key Responsibilities

End-to-End Machine Learning Delivery

1. Lead the full ML lifecycle: data acquisition → feature engineering → model development → validation → deployment → monitoring and iteration.
2. Design and implement predictive, prescriptive, and optimization models for manufacturing operations, predictive maintenance, quality, and supply-chain use cases.
3. Apply classical ML and statistical techniques, including: time-series forecasting; anomaly and outlier detection; regression, classification, clustering; probabilistic and statistical modeling and; optimization and decision models.

Data & Feature Engineering

1. Build and maintain robust ETL/ELT pipelines for batch and near-real-time ML workloads.
2. Perform exploratory data analysis (EDA) to understand data quality, bias, distributions, and feature behavior.
3. Design feature pipelines and feature stores, ensuring consistency between training and inference.
4. Integrate data from heterogeneous sources (APIs, databases, event streams) with strong validation, schema enforcement, and observability.

Production AI ML Systems

1. Deploy models as scalable services (APIs, batch jobs, streaming inference) in cloud environments.
2. Implement model monitoring for performance, drift, data quality, and operational health.
3. Collaborate with platform and software teams to ensure solutions are secure, reliable, and cost-efficient.
4. Contribute to and enforce MLOps best practices, including CI/CD for ML, reproducibility, versioning, and rollback strategies.

Technical Leadership & Collaboration

1. Translate ambiguous business problems into clear ML problem formulations, success metrics, and delivery plans.
2. Lead technical decision-making around model choice, evaluation strategy, and trade-offs.
3. Communicate model behavior, limitations, and results clearly to non-ML stakeholders using visualizations, dashboards, and written documentation.
4. Mentor junior engineers and contribute to ML engineering standards and best practices.

Required Qualifications (Must-Have)

1. 10+ years of combined experience across machine learning engineering, data engineering, and production system delivery.
2. 5+ years of hands-on experience delivering end-to-end AI ML systems in production, beyond experimentation or notebooks.
3. Demonstrated depth in classical machine learning and applied statistics (not limited to GenAI or LLMs).
4. Strong understanding of model evaluation, validation strategies, bias/variance trade-offs, and failure modes.
5. Strong proficiency in Python for production ML (clean code, testing, packaging, performance).
6. Strong SQL skills and experience modeling data for analytics and ML.
7. 3+ years of cloud experience (GCP strongly preferred; AWS acceptable).
8. Hands-on experience with: BigQuery, Cloud Storage, Dataflow / Apache Beam, Airflow / Cloud Composer, Containerized deployments (Docker; Kubernetes or managed equivalents).
9. Experience operationalizing ML with tools such as MLflow, Airflow/Dagster, CI/CD pipelines, and monitoring frameworks.
10. Proven ability to monitor, debug, and improve models post-deployment, including drift detection and retraining strategies.
11. Experience optimizing pipelines and models for performance, reliability, and cost in production environments.
12. Proven ability to translate business problems into ML solutions, defining success metrics and delivery milestones.
13. Strong communication skills: able to explain ML outcomes and trade-offs to technical and non-technical audiences.
14. Experience collaborating with cross-functional teams (engineering, operations, product). Nice-to-Have
15. Experience in manufacturing, industrial, or supply-chain domains.
16. Prior ownership of a technical roadmap or AI product lifecycle, including planning, prioritization, and delivery.
17. GCP or AI/ML professional certifications (e.g., Google Professional Machine Learning Engineer, Professional Data Engineer).
18. Background in consulting or client-facing delivery roles.
19. Limited GenAI/LLM experience is acceptable when complemented by strong classical ML depth (e.g., LLMs used as components, not the core skillset).