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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying AI solutions, with a focus on GenAI applications and robust production systems. Proficient in establishing evaluation metrics, managing CI/CD pipelines, and translating business challenges into technical roadmaps.
Highest-signal resume keywords
AI System DevelopmentGenAI Application ProductionMLOps and LLMOpsPython ProgrammingClient-Facing Consulting
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI Solution DesignRAG ArchitectureAgentic WorkflowsModel VersioningExperiment TrackingScalable Data PipelinesAutomated MonitoringCI/CD Pipeline DevelopmentEvaluation Metrics EstablishmentFine-Tuning GenAI Applications
Soft Skills
Client EngagementProject LeadershipTechnical Communication
Tools & Technologies
DatabricksPySparkSQLDelta LakeUnity CatalogMLflowMosaic AIOpenAIAzure AIHugging Face
Certifications & Qualifications
Databricks Certification
Industry Keywords
AI SolutionsGenAI FrameworksLLMOpsMLOpsProduction-Grade Applications
Tech Stack
Tools & technologiesAWSAzureGoogle Cloud PlatformPySparkPythonPyTorchSQLTensorflowUnity
About the role
Key responsibilities & impact- Embed with clients to design and build AI solutions that solve real business problems
- Turn AI capabilities into real-world applications used by customers and stakeholders
- Move AI solutions from experimentation and prototypes into robust, production-ready systems
- Prove AI systems work through evaluation and make them measurably better
- Design and implement RAG architectures, agentic workflows and fine-tuned GenAI applications
- Structure and orchestrate AI-driven applications using GenAI frameworks
- Establish evaluation baselines and metrics for prompts, retrieval, models and parameters
- Build and manage experiment tracking, model versioning, observability and deployment workflows
- Develop automated monitoring, evaluation and CI/CD pipelines for ML and LLM systems
- Design scalable training and inference pipelines
- Advise clients, translate business challenges into technical roadmaps and lead projects from scoping through delivery
- Explain architecture decisions to CTOs and pair-program with engineers to debug pipelines
- Work hands-on with Databricks, PySpark, SQL, Delta Lake, Unity Catalog, MLflow and Mosaic AI features
Requirements
What you’ll need- Experience building AI systems across the full lifecycle, from experimentation to deployment and monitoring
- Experience building production-grade GenAI applications, including RAG architectures, agentic workflows and fine-tuning
- Experience with platforms such as OpenAI, Anthropic, Azure AI or similar
- Experience with GenAI frameworks such as LangChain, LangGraph, Pydantic AI, Hugging Face or DSPy
- Evaluation-driven approach: establish baselines, define meaningful metrics and measure improvements
- Understanding of LLMOps and MLOps, including experiment tracking, model versioning, observability and deployment
- Experience building automated monitoring, evaluation and CI/CD pipelines for traditional ML and LLMs
- Experience designing scalable training and inference data pipelines for production
- Extensive Python experience with data/ML libraries and ability to write clean, testable, well-structured code
- Experience deploying or working with AI solutions in AWS, Azure or GCP
- Client-facing consultant mindset: translate business challenges into technical roadmaps and lead projects from scoping to delivery
- Databricks experience or Databricks certification is nice to have
- Experience with PyTorch or TensorFlow is nice to have
- Experience with production LLMOps tooling, including evaluation, monitoring and drift detection, is nice to have
Benefits
Comp & perks- Employment facilitated through a third-party Employer of Record/payroll provider in line with Hungarian employment requirements
- Access to Databricks learning resources, certifications and partner academies
- Regular internal knowledge-sharing sessions and workshops
- Opportunities to work hands-on with modern data and AI technologies, including Databricks and the wider data engineering ecosystem
- Flexible and collaborative working environment, with remote and hybrid working options
- International team collaboration with experienced data and AI professionals across Europe
- Growth opportunities to develop technical expertise, consulting skills and technical leadership
- Regular team activities and opportunities to connect with colleagues across the wider RevoData team
- Sustainable ways of working and wellbeing-focused environment
- Seven-month crash course covering certifications, hands-on client project work and internal knowledge-sharing sessions for candidates without Databricks experience, transitioning to a permanent contract upon agreed milestones