AI Senior Consultant

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.

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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

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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.

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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.

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How You’ll Add Value

1. Translate business challenges into AI‑driven products that are production‑ready, secure, and measurable.
2. Accelerate time‑to‑value by automating data pipelines, model training, and deployment using modern MLOps practices.
3. 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.
4. Shape the platform roadmap by integrating feature stores, vector stores, and lakehouse architectures that future‑proof the client’s data ecosystem.

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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!