Take ownership of the design and implementation of modern AI stack components, including data ingestion for AI/ML workloads and end-to-end model training and serving pipelines.
Build and manage fault-tolerant AI platforms that scale economically. You will balance the maintenance of legacy models with the rapid development of advanced, scalable solutions.
Provide technical mentorship to junior engineers and foster a collaborative environment. You will act as a bridge between data science and production engineering.
Promote best practices in coding, testing, and MLOps. You thrive in ambiguous conditions by independently identifying opportunities to optimize model pipelines and improve AI workflows.
Partner with data scientists, product managers, and software engineers to translate business needs into technical requirements and integrate AI solutions into production applications.
Enforce model quality standards, integrity, and reliability. You will be responsible for implementing model lineage, fairness, and privacy controls within the automated pipelines.
Build monitoring frameworks to track model performance and system KPIs, ensuring our AI initiatives drive measurable business outcomes.

Minimum of 4–6 years of professional experience in machine learning engineering, with a proven track record of deploying models into production environments.
Degree/Diploma in Computer Science, Engineering, Data Science, Applied AI, Machine Learning, or some combination.
Deep understanding of the modern AI stack, including data ingestion workflows and experience working with curated data warehouses like Snowflake, Databricks, or Redshift.
At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC) using Terraform.
High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles.
Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to support the end-to-end machine learning lifecycle.
Familiarity with model governance practices (lineage, fairness, and privacy) and experience using data cataloging tools for compliance.
Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction.
Experience in FinTech or SaaS environments is a significant advantage.

Machine Learning Engineer II

Key skills