AI/ML Engineer (MLOps)

Location: Poland; remote work available

Company

hirify.global is a data and technology services company established in 2008 with more than 1,800 talents across seven global sites.

What you will do

• Work with Data Science teams to implement machine learning models in production.
• Design, deliver, and manage industrialized data processing pipelines.
• Define and implement best practices for ML model lifecycles, MLOps, and LLMOps.
• Implement AI, MLOps, and LLMOps frameworks and support Data Science teams.
• Gather technical requirements, estimate planned work, and present solutions and results to internal and external clients.
• Create technical documentation and contribute to GenAI solutions and ML/AI automation where applicable.

Requirements

• Experience in data engineering, data processing, designing data pipelines, and at least one of these areas: Data Warehouse, Data Lake, Data Integration, Data Governance, Machine Learning, Deep Learning, or MLOps.
• Experience developing production-ready Python code, including microservices or APIs, and production-ready ML-related code.
• Practical experience with MLOps/LLMOps tools such as AzureML or AzureAI, and with Databricks.
• Good understanding of ML/AI concepts, including algorithm types, machine learning frameworks, model efficiency metrics, model lifecycles, and AI architectures.
• Good understanding of cloud concepts and architectures, with working knowledge of selected cloud services, preferably Azure or GCP.
• Practical experience with Spark/PySpark and Hive within Big Data platforms such as Databricks, EMR, or similar.
• Good communication, teamwork, support, responsibility for deliverables, problem-solving, and critical-thinking skills.
• Fluency in written and spoken English.

Nice to have

• Experience designing and delivering GenAI solutions.
• Practical implementations of LLM, ML, or AI automation for scale and efficiency.
• Experience with GenAI technologies such as ChatGPT, Gemini, RAGs, or prompt engineering.
• Experience designing and programming ML algorithms and data processing pipelines in Python.
• Understanding of CI/CD and DevOps concepts, with experience using tools such as GitHub Actions, GitLab, or Azure DevOps.
• Experience productizing ML solutions with Spark/Databricks or Docker/Kubernetes.

Culture & Benefits

• Office-as-an-option model: choose remote work or work from the office.
• Workation is available in line with the workation policy.
• Flexible working hours and preferred contract form.
• Stable employment, online onboarding with a buddy, and cooperation with engineers and experts.
• Access to learning platforms, certificate training, upskilling programs, Competency Centers, knowledge-sharing sessions, and community webinars.
• Inclusive, values-driven community with well-being support, modern equipment, and opportunities to participate in charitable and environmental initiatives.