Senior ML/AI Engineer

Responsibilities:
    •    Design, develop, and deploy machine learning models to solve business problems such as revenue forecasting and member risk prediction (e.g. churn).
    •    Build end-to-end ML pipelines from data ingestion to model deployment using AWS services.
    •    Develop and deploy LLM-based applications and AI agents on AWS Bedrock.
    •    Implement RAG (Retrieval-Augmented Generation) architectures for enterprise AI use cases.
    •    Work with large volumes of data to perform feature engineering, data preprocessing, and model optimization.
    •    Write optimized SQL queries for analytics, model training datasets, and data validation.
    •    Build scalable ML APIs and microservices for real-time and batch inference.
    •    Collaborate with engineering, product, and data teams to define ML-driven solutions.
    •    Apply MLOps best practices for versioning, monitoring, CI/CD, and production reliability.
    •    Conduct A/B testing, model evaluation, and model performance tracking.
    •    Document solutions and communicate progress clearly in strong English.

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Must Have Qualifications:
    1.    5+ years of hands-on experience in Machine Learning development and deployment in production.
    2.    Strong expertise in XGBoost and classical ML algorithms (LightGBM, Random Forest, etc.).
    3.    Proven experience with predictive modeling (forecasting, classification, risk scoring).
    4.    Strong SQL skills (data analysis, ETL, performance tuning).
    5.    Advanced Python skills (pandas, NumPy, scikit-learn).
    6.    Hands-on experience with AWS Bedrock and building AI/ML agents.
    7.    Strong AWS experience building solutions using S3, Lambda, Glue, Step Functions.
    8.    Experience applying MLOps concepts (CI/CD pipelines, Deployment, Monitoring).
    9.    Strong English level required (written and spoken).
    10.    Ability to work independently and solve complex problems.

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Preferred Qualifications:
    1.    AWS certifications (ML Specialty, Solutions Architect is a plus).
    2.    Experience with Amazon SageMaker (training, tuning, deployment).
    3.    Experience building RAG pipelines with embeddings and vector search.
    4.    Knowledge of Amazon Redshift, RDS, and AWS data architecture.
    5.    Experience with Docker and containerized deployments.
    6.    Familiarity with MLflow, Kubeflow, or SageMaker Pipelines.
    7.    Experience working with enterprise-scale datasets and distributed computing.
    8.    Background in Generative AI or NLP systems development.
    9.    Knowledge of API development using FastAPI or Flask.
    10.    Familiarity with Git, Jira, and Agile development processes.

Preferred working hours: 11am-7pm CET