- Company Name
- Saegus
- Job Title
- Consultant·e Data Scientist & AI Engineer - Senior
- Job Description
-
**Job Title**
Senior Consultant, Data Science & AI Engineer
**Role Summary**
Senior consultant responsible for collaborating with clients to design, implement, and industrialize high‑impact Data & AI projects (machine learning, deep learning, generative AI). Acts as bridge between business units and technical teams, ensuring alignment of solutions with business objectives, ROI, and compliance with client IT guidelines. Leads knowledge sharing, training, and innovation within the Smart Data practice and may supervise junior consultants.
**Expectations**
- Minimum 4 years of end‑to‑end Data Science and AI experience, including leadership in consulting engagements.
- Proven track record of delivering production‑ready generative AI and machine‑learning solutions, with full lifecycle management (MLflow, MLOps, CI/CD).
- Strong technical depth in RAG systems, foundation models, and cloud‑native MLOps.
- Ability to mentor and elevate consultant skill levels while actively contributing to client projects.
- Fluency in French and English (written & spoken).
**Key Responsibilities**
- Partner with clients to scope, design, and execute Data & AI initiatives that deliver measurable business value.
- Translate business requirements into data pipelines, model architectures, and scalable cloud deployments.
- Develop and operationalize RAG document systems, foundation‑model‑based generative solutions, and automated ML pipelines.
- Orchestrate cloud infrastructure (Azure or GCP), IaC (Terraform), data services (Snowflake, Azure Data Lake, Copilot), and monitoring (MLflow, Application Insights).
- Facilitate knowledge transfer through workshops, demos, and best‑practice documentation.
- Maintain agile project rhythm, deliverables, and compliance with security and regulatory standards.
**Required Skills**
- Strong programming: Python (data science & MLE), Bash/Shell scripting.
- MLOps expertise: MLflow, CI/CD (GitLab), ops orchestration, model monitoring, testing.
- Foundation model proficiency: OpenAI, Anthropic, Gemini, Llama, Mistral, etc., plus orchestration frameworks (LangChain, LangGraph).
- Cloud platform: Azure or GCP, with Terraform IaC.
- Data engineering: Snowflake, Azure Synapse, Data Factory, pipelines, ETL.
- Algorithms: clustering, regression, classification, forecasting, NLP, computer vision.
- Strong analytical, problem‑solving, and communication skills; ability to convert technical concepts to business language.
**Required Education & Certifications**
- Bachelor’s or Master’s degree (or equivalent) in Engineering, Computer Science, Data Science, Business Analytics, or related field.
- Domain certification in Data Science, AI, or ML is a plus (e.g., Microsoft Certified: Azure AI Engineer Associate, Google Professional Data Engineer, AWS Certified Machine Learning).
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