GCP AI Developer

Be in IT is a recruitment company specializing in IT specialists. The recruiter has been active in IT recruitment since 2016 and operates as a freelancer since 2021, running their own recruitment company. They focus on comprehensive support for clients' IT technology needs and emphasize creating precise and comprehensive job offers. The recruiter holds a certificate from the National Employment Agency Register (number 27663).

• Documented experience as AI/ML Developer building and deploying models on Google Cloud Platform (GCP) with practical knowledge of Vertex AI and related GCP AI services.
• Good knowledge of machine learning frameworks (TensorFlow, PyTorch, or JAX) and MLOps practices including model versioning, monitoring, and retraining strategies.
• Proficiency in Python with experience writing production code, including REST API and microservices.
• English language proficiency at B2 or C1 level for communication in an international environment.

Nice to have:

• GCP Professional certification.
• Experience with generative AI, LLM models, RAG architectures, and prompt engineering.
• Azure or AWS architectural certifications.
• Experience with containerization (Docker, Kubernetes, Cloud Run) and CI/CD pipelines.
• Knowledge of data governance principles and responsible AI frameworks.
• Higher education in Computer Science, Data Science, Mathematics, or related fields.
• Availability ASAP; acceptable notice period up to one month ending at month-end.

• Long-term cooperation with an international consulting company serving automotive, healthcare, finance, and manufacturing sectors.
• Designing, building, and deploying AI/ML models and generative AI solutions using native GCP services: Vertex AI, BigQuery ML, Document AI, Gemini API.
• Creating and maintaining end-to-end ML pipelines: data ingestion, feature engineering, training, evaluation, and model serving.
• Ensuring production standards: scalability, monitoring, and responsible AI.
• Tracking innovations in GCP AI and industry trends and implementing best practices.
• Collaborating with data architects, data scientists, and business stakeholders to deliver value-generating solutions.