C
AI Senior Consultant
Capgemini
Hybrid Senior From €66k/yr 2w ago
About the role
AI Senior Consultant – Role Overview
| What you’ll do | Why it matters |
|---|---|
| Design end‑to‑end AI use cases – from data foundation, through model development, to production‑ready cloud services. | Turn complex business problems into scalable, value‑creating AI solutions. |
| Choose your specialization – • AI/ML Engineering (copilots, agents, MLOps) • Data Analytics & BI (dashboards, KPI modelling) • Cloud Platform Engineering (Lakehouse, Feature/Vector Stores). |
Leverage your strongest skills while still contributing to the broader AI practice. |
| Own a work‑stream – lead the technical delivery, coordinate with stakeholders, and ensure quality, timelines, and budget are met. | Demonstrates leadership and accountability across the project lifecycle. |
| Collaborate cross‑functionally – work with data engineers, domain experts, product owners, and client teams. | Guarantees that solutions are aligned with business goals and technical standards. |
| Drive continuous improvement – embed observability, guardrails, automated testing, and monitoring into every solution. | Guarantees reliability, compliance, and long‑term maintainability of AI services. |
| Mentor & up‑skill – coach junior consultants, share best practices, and contribute to internal knowledge bases. | Builds a strong, future‑proof consulting practice. |
Focus Areas – What you’ll actually build
| Focus | Typical Deliverables | Key Technologies |
|---|---|---|
| AI/ML Engineering | • Production‑ready LLM‑based copilots & autonomous agents • End‑to‑end training pipelines (data prep → model → CI/CD) • Evaluation & monitoring dashboards, bias/guardrail frameworks |
Python, PyTorch/TensorFlow, HuggingFace, LangChain/LlamaIndex, Pinecone/FAISS, MLflow, Azure/AWS/GCP, Docker/K8s |
| Data Analytics & BI | • Interactive Power BI / Tableau dashboards • Data models, semantic layers, KPI definitions • Self‑service analytics portals, data‑quality reports |
Power BI, Tableau, SQL, Snowflake, Azure Synapse, Dataiku/Alteryx, ETL/ELT tools |
| Cloud Platform Engineering | • Managed Lakehouse on Databricks or Snowflake • Feature‑store & Vector‑store services • Automated data‑engineering & AI workflows (Airflow, Prefect) • Secure, cost‑optimized multi‑cloud environments |
Azure/AWS/GCP, Databricks, Snowflake, Apache Spark, Terraform, CI/CD (GitHub Actions, Azure DevOps), MLflow, S3/ADLS, Kubernetes |
Required Qualifications
| Core | Details |
|---|---|
| Experience | ≥ 4 years in Data Science, AI Engineering, or Cloud Platform Engineering; proven ownership of project work‑streams; experience leading small‑to‑medium teams. |
| Technical Skills | • Python (core) • One of the major ML frameworks (PyTorch/TensorFlow) • Familiarity with LLM‑RAG pipelines (LangChain/LlamaIndex) • Vector DBs (Pinecone, FAISS) • MLOps tools (MLflow, CI/CD) • Cloud services (Azure, AWS, GCP) • SQL & modern data‑warehousing (Snowflake, Databricks) • (If BI focus) Power BI/Tableau, data modelling, KPI design. |
| Soft Skills | Analytical thinking, structured problem‑solving, strong communication in German & English, team‑oriented, proactive “hands‑on” attitude. |
| Education | Bachelor/Master in Computer Science, Data Science, Engineering or related field. |
What You’ll Get
| Compensation & Benefits | Details |
|---|---|
| Base Salary | €66 000 gross / yr (adjustable upward based on experience & skill set). |
| Flexibility | Home‑office & flexible hours; FlexAbroad program for EU‑based remote work. |
| Work‑Life Balance | Wellness options (massage, yoga), monthly meal vouchers, Wiener Linien pass, office massages, regular team events. |
| Professional Growth | Access to certification programs, cutting‑edge tech training, internal knowledge‑sharing forums, mentorship. |
| Equipment | Company notebook for private use, high‑performance workstation, VPN & cloud credits for experimentation. |
| Culture | Collaborative, innovation‑driven environment; emphasis on continuous learning and employee well‑being. |
How You’ll Add Value
- Translate business challenges into AI‑driven products that are production‑ready, secure, and measurable.
- Accelerate time‑to‑value by automating data pipelines, model training, and deployment using modern MLOps practices.
- Elevate client confidence through transparent monitoring, bias mitigation, and robust governance frameworks.
4 Foster a data‑centric culture by delivering intuitive analytics dashboards and empowering stakeholders with self‑service insights. - Shape the platform roadmap by integrating feature stores, vector stores, and lakehouse architectures that future‑proof the client’s data ecosystem.
Next Steps
If this description resonates with your experience and career aspirations, let’s discuss which focus area aligns best with your strengths and how you can start driving AI transformation for our clients. Feel free to share your CV, a brief cover note highlighting your preferred specialization, and any certifications you already hold (e.g., Azure AI Engineer, SnowPro Core, TensorFlow Developer).
Looking forward to building the future of AI together!
Requirements
- You are passionate about AI, data, and modern cloud technologies, and you want to translate complex challenges into scalable solutions.
- Knowledge of common tools & technologies depending on the focus, e.g., AI/ML Engineering focus: Python, PyTorch/TensorFlow, HuggingFace, RAG‑Pipelines & Agent frameworks (e.g., LangChain, LlamaIndex), vector databases (e.g., Pinecone, FAISS), MLOps & automation (MLflow, CI/CD, monitoring, evaluation pipelines).
- Focus on Data Analytics & BI: Power BI, Tableau, modern dashboard & reporting tools, SQL, data modeling, semantic layer & KPI definition, ETL/ELT tools, data profiling & data governance.
- Focus on Cloud Platform Engineering: Cloud services on Azure, AWS, or GCP, Databricks, Snowflake, Spark & Lakehouse architectures, modern platform patterns (Feature Stores, Vector Stores, Data Lakehouses).
- Analytical thinking, structured working approach, and high problem-solving skills.
- Team-oriented mentality, confident demeanor, and hands-on approach.
- Very good spoken and written German and English skills.
Responsibilities
- Design end-to-end AI use cases – from the data foundation through modeling and engineering to scalable cloud platforms.
- Development of copilots, agents, and production-ready AI services, working with modern cloud ecosystems (Azure, AWS, or GCP), automating training, deployment, monitoring, and scaling, building guardrails, observability concepts, and evaluation pipelines.
- Development of modern analytics and dashboard solutions (e.g., Power BI, Tableau), data modeling, KPI definition, and derivation of business insights, consulting departments on data-driven decision-making processes, and further development of existing data products towards future ML and GenAI use cases.
- Building and operating cloud-based data and AI platforms (e.g., Databricks, Snowflake), automating and scaling Data Engineering, Analytics, and AI workflows, integrating modern platform patterns such as Feature Stores, Data Lakehouses, or Vector Stores.
Benefits
MassageYogaCompany Notebook for Private UseMeal VouchersFlexible Working Hourshome officeflexible working hoursFlexAbroad program
Skills
AWSAzureDatabricksGoogle Cloud PlatformHuggingFaceLangChainLlamaIndexMLflowPineconePower BIPyTorchPythonSnowflakeSparkSQLTableauTensorFlow
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