Senior ML/AI Engineer

We are looking for a Senior ML/AI Engineer to join the AI Engineering team within the Data Science & AI Competency Center. This role focuses on implementing Machine Learning models into production, designing and delivering GenAI solutions, and establishing strong ML/LLM operational standards. You will work closely with Data Science teams to industrialize ML pipelines, scale AI systems, and embed best practices across the ML lifecycle.

This is a fully remote role based in Poland.

Role Details

Location: Remote (Poland)

Employment Type: Full-time

Seniority Level: Senior

Start Date: Flexible (1-month notice preferred; up to 3 months acceptable)

The company has offices in Warsaw and Lublin. Candidates must have existing work authorization for Poland. Visa sponsorship is not specified.

The role is open to strong mid-level and outstanding junior candidates if they demonstrate high potential.

Mission & Context

The team delivers business solutions using Machine Learning and Data Science, with a strong focus on Forecasting and Customer Analytics. This role supports end-to-end ML and GenAI solution delivery — from technical architecture and deployment pipelines to operational excellence and scalability. The organization emphasizes diversity, equity, and inclusion and operates within a values-driven, collaborative environment.

Key Responsibilities

• Work closely with Data Science teams to implement ML models into production
• Design and deliver GenAI solutions
• Build practical, scalable implementations of LLM/ML/AI automation
• Design, deliver, and manage industrialized processing pipelines
• Define and implement best practices across the ML model lifecycle
• Implement AI/MLOps/LLMOps frameworks and support teams in operational standards
• Apply modern techniques, tools, and frameworks in ML Architecture and Operations
• Gather technical requirements and estimate delivery efforts
• Present technical solutions and results to internal and external stakeholders
• Create comprehensive technical documentation
  
  
  

Required Skills

• Python
• MLOps / LLMOps tools (AzureML / AzureAI strongly preferred)
• Databricks
• Spark / PySpark
• Cloud platforms (Azure or GCP)
  
  
  

Requirements

• 5+ years of Data Engineering experience
• 5+ years of production-ready Python development
• 3+ years of production-level ML-related code development
• 1+ year of hands-on GenAI / LLM production implementation
• Practical experience with MLOps / LLMOps tools (AzureML / AzureAI required)
• Strong experience working with Databricks and Spark / PySpark
• Experience working on major cloud platforms (Azure or GCP)
• Without hands-on experience in MLOps/LLMOps tools, Databricks, Spark/PySpark, and cloud infrastructure, candidates should not proceed.
  
  
  

Candidate Profile

• Has a strong Data Engineering foundation
• Is experienced in scaling ML systems into production
• Has real production experience with GenAI / LLM-based systems
• Understands ML lifecycle governance and operationalization
• Can independently design scalable ML architectures

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• Is comfortable presenting technical concepts to clients and stakeholders
• Operates with strong ownership and documentation discipline
  
  
  

Benefits

• Stable employment
• Fully remote or office-based model
• Flexible working hours and contract type
• Workation policy
• Comprehensive onboarding with assigned Buddy
• Unlimited access to Udemy from day one
• Certificate training programs and upskilling support
• Capability development programs and Competency Centers
• Knowledge-sharing sessions and community webinars
• 110+ training opportunities annually
• Internal promotions (76% of managers promoted internally)
• Referral bonuses
• Health and well-being initiatives
• Inclusive, diverse, and values-driven culture
• Modern office equipment
• If useful, I can also prepare:
• A technical screening questionnaire (GenAI + MLOps focused)
• A candidate evaluation scorecard
• A sourcing brief for senior ML engineers in Poland
• A structured interview guide (ML architecture + operational depth)