Machine Learning Engineer
As a Machine Learning Engineer, you will be a key contributor to the design, development, and deployment of our foundational AI and ML models. You will build robust, scalable machine learning pipelines and platforms that support advanced analytics and business intelligence. This role is perfect for an experienced person who wants to ensure our ML systems are efficient, reliable, and deeply integrated into our organizational goals.
Here's How You Make an Impact:
Develop & Deploy: Focus on the hands-on building, training, and operational deployment of machine learning models, ensuring they perform reliably within existing production environments.
Champion Technical Standards: Advocate for top-tier practices across coding, testing, and MLOps processes. Navigate ambiguity autonomously to refine pipelines and elevate ML engineering workflows.
Optimize & Scale: Construct resilient, cost-efficient ML & AI use cases. Balance sustaining established models with accelerating the rollout of highly scalable, modern systems.
Partner & Collaborate: Team up with cross-functional stakeholders, including risk specialists, product leads, and software developers, to convert strategic needs into technical specs and smoothly embed ML features into live applications.
Establish Controls & Governance: Uphold stringent benchmarks for model dependability, fairness, and compliance. Direct the integration of lineage tracking and data protection workflows into our automated systems.
Track & Evaluate: Formulate comprehensive observability systems to capture model health and key operational metrics, ensuring machine learning investments yield quantifiable organizational value.
You Thrive Here By Possessing the Following:
Experience: Minimum of 3-5 years of professional experience in machine learning engineering, with a proven track record of deploying models into production environments.
Technical Depth: Deep understanding of the modern data stack, including data ingestion workflows and experience working with curated data warehouses like Databricks or Redshift.
Cloud Proficiency: At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC), Terraform.
Orchestration Expert: High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles.
MLOps Toolkit: Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to support the end-to-end machine learning lifecycle.
Governance Mindset: Familiarity with model governance practices (lineage, fairness, and privacy) and experience using data cataloging tools for compliance.
Communication: Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction.
Industry Context: Experience in FinTech or Financial Risk environments is a significant advantage.