Lead ML Engineer

Cloud Hybrid Technologies, LLC; Dallas (TX); On-site; USD 130,000 - 160,000; Full time

Job summary

Cloud Hybrid Technologies, LLC is seeking a highly skilled Lead Machine Learning Engineer to drive advanced AI and machine learning initiatives within their banking and finance operations. Ideal candidates will have over 10 years of hands-on machine learning experience, particularly in regulated environments, and demonstrate proficiency in Python or R. Responsibilities include leading machine learning projects, developing predictive models, and mentoring junior staff. The firm values diversity in its workforce, promoting equal opportunity across all employment decisions.

Qualifications

• Minimum 10 years of hands-on experience in machine learning, artificial intelligence, or data science roles.
• Experience in the banking or finance sector with regulatory compliance knowledge.
• Strong programming proficiency in Python, R, or Scala.
• Expertise in leading ML libraries and frameworks such as TensorFlow or PyTorch.
• Experience with big data technologies like Hadoop and Spark.
• Solid understanding of SQL and/or NoSQL databases.
• Background in risk modeling and capital models relevant to banking.
• Practical experience with machine learning operationalization tools.
• Exceptional communication skills to convey complex concepts.

Responsibilities

• Lead end-to-end machine learning projects from problem definition to deployment.
• Design and implement predictive models and risk modeling frameworks.
• Collaborate to identify opportunities for machine learning applications.
• Ensure compliance of ML solutions with industry regulations.
• Oversee operationalization of models using ML ops tools.
• Present findings and recommendations to non-technical stakeholders.
• Mentor junior team members and promote best practices.

Skills

Machine learning experience

Artificial intelligence

Data science

Python

R

Scala

TensorFlow

PyTorch

Scikit-learn

Hadoop

Spark

SQL

NoSQL

Risk modeling

ML ops tools

Statistical modeling

Optimization techniques

Feature engineering

Communication skills

Tools

Docker

Kubernetes

MLflow

Kubeflow

Airflow

Job description

We are seeking a highly skilledLead Machine Learning Engineerto driveadvanced AI and machine learning initiativeswithin our banking and finance operations. This hands‑on leadership role demands technical excellence, project ownership, and the ability to communicate complex concepts to diverse stakeholders. The ideal candidate will bring deep expertise in developing and deploying machine learning solutions, particularly in regulated financial environments.

Required Skills and Experience:

• Minimum 10 years of hands‑on experience in machine learning, artificial intelligence, or data science roles.
• Demonstratedexperience in the banking or finance sector,with a strong understanding of regulatory compliance.
• Advanced programming proficiency inPython, R, or Scala.
• Expertise in leading ML libraries and frameworks:TensorFlow, PyTorch, Scikit‑learn.
• Experience working with big data technologies such asHadoop and Spark.
• Solid knowledge ofSQL and/or NoSQLdatabase systems.
• Background in risk modeling, capital models, and regulatory frameworks relevant to banking.
• Practical experience withML ops tools (Docker, Kubernetes, MLflow, Kubeflow, Airflow).
• Strong skills instatistical modeling, optimization techniques, and feature engineering.
• Exceptional ability to communicate technical concepts and results to non-technical audiences.

Key Responsibilities:

• Lead end-to-end machine learning projects, from problem definition through deployment and monitoring.
• Design, develop, and implement robust predictive models and risk modeling frameworks for banking and capital management.
• Collaborate with data engineers, analysts, and business units to identify opportunities for machine learning applications.
• Ensure all ML solutions comply with industry regulations and internal risk management standards.
• Oversee the operationalization of models using ML ops tools (Docker, Kubernetes, MLflow, Kubeflow, Airflow).
• Present findings, model outcomes, and recommendations clearly to non-technical stakeholders and senior management.
• Mentor junior team members and foster best practices in statistical modeling, feature engineering, and optimization.

Cloud Hybrid is an equal opportunity employer inclusive of female, minority, disability and veterans, (M/F/D/V). Hiring, promotion, transfer, compensation, benefits, discipline, termination and all other employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity, age, disability, national origin, citizenship/immigration status, veteran status or any other protected status. Cloud Hybrid will not make any posting or employment decision that does not comply with applicable laws relating to labor and employment, equal opportunity, employment eligibility requirements or related matters. Nor will Cloud Hybrid require in a posting or otherwise U.S. citizenship or lawful permanent residency in the U.S. as a condition of employment except as necessary to comply with law, regulation, executive order, or federal, state, or local government contract