Senior ML AI Engineer

1 tydzień temu

Puławska Warszawa, Polska Lingaro Pełny etat

The person we are looking for will become part of Data Science and AI Competency Center working in AI Engineering team.

Tasks:

  • Working with Data Science teams to implement Machine Learning models into production 
  • Design, delivery GenAI solutions  
  • Practical and innovative implementations of LLM/ML/AI automation, for scale and efficiency 
  • Design, delivery and management of industrialized processing pipelines 
  • Defining and implementing best practices in ML models life cycle and ML operations/LLM operations 
  • Implementing AI /MLOps/LLMOps frameworks and supporting Data Science teams in best practices 
  • Gathering and applying knowledge on modern techniques, tools and frameworks in the area of ML Architecture and Operations 
  • Gathering technical requirements & estimating planned work 
  • Presenting solutions, concepts and results to internal and external clients 
  • Creating technical documentation

Requirements:

  • At least 5+ years of Data engineering experience with last 3 years experience in building Data processing  
  • At least 5+ years of experience in production-ready Python code development (e.g., microservices, APIs, etc.) 
  • At least 3+ years of experience in production-ready ML-related code development 
  • At least 1+ years of experience with GenAI (ChatGPT, Gemini, RAGs, prompt engineering)
  • Practical experience in MLOps/LLMOps tools like AzureML/AzureAI 
  • Practical experience with Databricks 
  • Good understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model life-cycle, AI architectures 
  • Good understanding of Cloud concepts and architectures, as well as working knowledge with selected cloud services, preferably Azure or GCP 
  • Experience in at least one of following domains: Data Warehouse, Data Lake, Data Integration, Data Governance, Machine Learning, Deep Learning, MLOps 
  • Practical experience in Spark/PySpark and Hive within Big Data Platforms like Databricks, EMR or similar 
  • Experience in designing and implementing data pipelines 
  • Good communication skills 
  • Ability to work in a team and support others 
  • Taking responsibility for tasks and deliverables 
  • Great problem-solving skills and critical thinking 
  • Fluency in written and spoken English. 

Nice to have skills & knowledge:  

  • Experience in designing, programming ML algorithms, and data processing pipelines using Python 
  • Good understanding of CI/CD and DevOps concepts, and experience in working with selected tools (preferably GitHub Actions, GitLab, or Azure DevOps) 
  • Experience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes.

We offer:

  • Stable employment. On the market since 2008, 1500+ talents currently on board in 7 global sites.
  • "Office as an option" model. You can choose to work remotely or in the office, depending on your location. 
  • Flexibility regarding working hours and your preferred form of contract. 
  • Comprehensive online onboarding program with a "Buddy" from day 1.  
  • Cooperation with top-tier engineers and experts. 
  • Unlimited access to the Udemy learning platform from day 1.
  • Certificate training programs. Lingarians earn 500+ technology certificates yearly. 
  • Upskilling support. Capability development programs, Competency Centers, knowledge sharing sessions, community webinars, 110+ training opportunities yearly.
  • Internal Gallup Certified Strengths Coach to support your growth.
  • Grow as we grow as a company. 76% of our managers are internal promotions.  
  • A diverse, inclusive, and values-driven community.  
  • Autonomy to choose the way you work. We trust your ideas. 
  • Create our community together. Refer your friends to receive bonuses. 
  • Activities to support your well-being and health.
  • Plenty of opportunities to donate to charities and support the environment. 
  • Modern office equipment. Purchased for you or available to borrow, depending on your location.

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