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.
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