- Company Name
- SOFTEAM Expertise Data & IA
- Job Title
- Spécialiste en intelligence artificielle
- Job Description
-
**Job Title:** Senior AI / ML Ops Engineer – Data & Artificial Intelligence
**Role Summary:**
Lead the technical design, development, and production‑grade deployment of AI/ML solutions (including ML, DL, Generative AI, and agentic models) for large financial institutions. Serve as the primary AI/ML Ops expert, ensuring model robustness, explainability, compliance, and operational excellence across the project lifecycle.
**Expectations:**
- 5‑8 + years of professional experience in AI engineering and ML Ops.
- Proven ability to translate business requirements into technical AI specifications.
- Strong technical leadership, mentorship of junior staff, and clear communication in English.
- Commitment to regulatory compliance (GDPR, EU AI Act) and internal governance standards.
**Key Responsibilities:**
- Elicit and formalize business use‑cases into detailed AI/Data technical specifications.
- Guide model design, development, and validation to ensure robustness, explainability, and scalability.
- Define and implement the ML Ops framework: versioning, reproducibility, CI/CD pipelines, and model governance.
- Build and maintain CI/CD pipelines (Docker, Kubernetes, GitHub Actions/Jenkins) for model deployment, monitoring, and rollback.
- Ensure model and deliverable quality, traceability, and compliance with internal standards, GDPR, and AI Act.
- Develop and disseminate AI best practices (feature engineering, data validation, code quality, security, reusability).
- Audit and certify models produced by data scientists before production release.
- Mentor and up‑skill junior engineers and data scientists in AI/ML Ops practices.
- Conduct continuous technology scouting on emerging AI frameworks, architectures, and tools.
**Required Skills:**
- Programming: Python, SQL, Bash.
- AI/ML Ops frameworks: MLflow, Kubeflow, Amazon SageMaker, Google Vertex AI, Airflow, DVC.
- Containerization & orchestration: Docker, Kubernetes, GitHub Actions, Jenkins.
- Data engineering: Apache Spark, PySpark, Databricks, data pipeline design.
- Model governance & compliance: GDPR, EU AI Act, internal audit processes.
- Software quality practices: automated testing, documentation, version control, reproducibility.
- Technical leadership, problem‑solving, and effective teamwork.
- Operational English (technical).
- Banking/financial domain experience is a plus.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Software Engineering, Data Science, or related field (Master’s preferred).
- Relevant certifications (e.g., AWS Certified Machine Learning – Specialty, Google Cloud Professional Data Engineer, Certified Kubernetes Administrator) are advantageous but not mandatory.