Overview We’re looking for a teammate with hands‑on experience building and shipping production ML systems, with deep proficiency in Python and the modern AWS ML stack. Key Qualifications Python Expertise: Python is your primary language. You write clean, well‑structured code and are comfortable owning end‑to‑end ML workflows — from data ingestion and EDA through model training, validation, and deployment. AWS SageMaker: Practical, hands‑on experience with SageMaker as your primary ML platform — including SageMaker Studio, Training Jobs, Pipelines, Model Registry, and real‑time or batch inference endpoints. Machine Learning Fundamentals: Strong grounding in supervised and unsupervised ML methods — gradient boosting, neural networks, dimensionality reduction, clustering, and survival/time‑to‑event models. Experience with scikit‑learn, XGBoost, LightGBM, and PyTorch or TensorFlow. Feature Engineering and Data Wrangling: Demonstrated ability to extract, clean, and engineer features from complex, multi‑source datasets using Python (pandas, numpy, PySpark) and SQL against platforms such as Snowflake or similar cloud data warehouses. Model Evaluation and Experimentation: Rigorous approach to model evaluation — cross‑validation, holdout testing, calibration, and business‑metric alignment. Experience with experiment tracking tools such as MLflow or SageMaker Experiments. Cloud and Infrastructure Awareness: Solid AWS experience beyond SageMaker, including S3, IAM, Lambda, and Step Functions. Familiarity with infrastructure‑as‑code or CI/CD patterns for ML pipelines is a plus. Data Platform Integrations: Hands‑on experience working with Snowflake, Apache Iceberg, or similar modern data platforms as upstream data sources for ML pipelines. Familiarity with Qlik Cloud Analytics or Qlik Talend Cloud is a strong plus. Bias for Impact: You care about whether your models actually change decisions — not just whether they score well on a leaderboard. Strong Communication: Ability to explain model behavior, limitations, and business implications to non‑technical stakeholders clearly and without jargon. Security and Governance Mindset: Awareness of responsible AI practices, data privacy considerations, model auditability, and the importance of reproducibility in production ML systems. Collaborative Spirit: Comfortable working across functions and levels, from data engineers and CSMs to the C‑suite. Job Responsibilities Build and deploy supervised and unsupervised ML models on AWS SageMaker — including classification, regression, clustering, and anomaly detection use cases. Own the feature engineering pipeline: develop robust, reusable feature pipelines in Python that transform raw data from Snowflake, our client Cloud Analytics, and other sources into high‑quality model inputs. Integrate with the data ecosystem: connect model pipelines to our client Cloud Analytics, Talend Cloud, Snowflake, and Apache Iceberg, ensuring data freshness, lineage, and governance standards. Operationalize models at scale: leverage SageMaker Pipelines, Model Registry, and endpoints to bring models into production reliably — with monitoring, drift detection, and retraining workflows in place. Support LLM‑augmented workflows: collaborate with AI Systems Engineers to integrate predictive model outputs as structured signals into agentic AI pipelines deployed on AWS Bedrock. Translate signals into action: partner with Customer Success, Sales, and Analytics stakeholders to translate model outputs into actionable insights, dashboards, and automated intervention triggers. Iterate and instrument: operate in a fast‑moving incubator environment — prototype quickly, measure model performance against business outcomes, and continuously refine based on real usage signals. Document and govern: maintain clear model cards, experiment logs, and data lineage documentation in support of our client’s AI governance framework and ISO 42001 compliance posture. Benefits What we offer: Culture of caring. Prioritize an inclusive culture of acceptance and belonging. Learning and development. Continuous learning opportunities and career growth. Interesting & meaningful work. Projects that matter and cutting‑edge solutions. Balance and flexibility. Work arrangement options to achieve work‑life balance. High‑trust organization. Integrity, trust, and ethical values. GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner to the world’s largest and most forward‑thinking companies. Since 2000, we’ve been at the forefront of the digital revolution. J-18808-Ljbffr