Principal AI/ML Engineer

Fidelity•Westlake, TX

About The Position

Builds and maintains large-scale machine learning infrastructure, ML, data, and application pipelines, and evaluates ML models using Apache Spark and distributed data-processing frameworks. Establishes a Python ML ecosystem using object-oriented programming in Java, Scala, and Python, as well as SQL and Unix scripting. Uses ETL/ELT, messaging and streaming technologies such as AWS SQS, Kinesis, and Kafka; relational and NoSQL databases; APIs; and in-memory technologies. Develops scalable distributed systems using open-source technologies and CI/CD tools. Applies business knowledge to translate divisional initiatives into business solutions, develops complex ML infrastructure, and evaluates alternatives. Analyzes and recommends changes to development policies, procedures, standards, and strategies. The role builds analytics, ML platforms, and software for model prediction and optimization; scales model training and deployment; implements big-data solutions; and designs data architectures and data-processing channels aligned with business requirements. It collaborates with business, systems analysis, and software engineering teams to integrate model outputs and design systems. The position also develops testing, validation, programming, and documentation procedures; advises senior management on technical strategy; mentors junior team members; and conducts complex technical and functional analysis while coordinating multiple project schedules and delivering against project goals, timelines, and budgets. Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Current onsite requirements vary by role and location and may evolve; this transition does not apply to fully remote roles. Fidelity will not provide immigration sponsorship for this position.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent), plus five years of experience as a Principal AI/ML Engineer or in a closely related occupation executing cloud-computing functions for distributed systems; or a master’s degree in one of those fields (or foreign education equivalent), plus three years of such experience.
Experience executing cloud-computing functions for distributed systems, including architecting CI/CD pipelines, building large-scale data-processing and analytics platforms, and developing advanced monitoring and observability systems in AWS.
Demonstrated expertise architecting, designing, and building scalable big-data frameworks and analytics platforms using Apache Spark and Kubernetes.
Demonstrated expertise developing and optimizing EMR workloads on EKS for petabyte-scale datasets and improving platform scalability and performance using AWS services including EMR, EKS, Lambda, CloudWatch, and SQS in a cloud-native environment.
Demonstrated expertise designing, implementing, and maintaining CI/CD pipelines for distributed systems using Git repositories and AWS CDK; automating deployment workflows and monitoring with AWS CloudWatch; and migrating on-premises systems to AWS while ensuring production scalability and reliability.
Demonstrated expertise conducting AI/ML research focused on reinforcement learning, developing algorithms using Monte Carlo Tree Search for decision-making in multi-agent systems, and implementing and validating RL models in simulation using Java, Python, and JMonkey Game Engine.
Demonstrated expertise designing real-time simulation platforms, implementing multi-agent RL models in large-scale simulations using Java-based 3D engines, and evaluating model performance and safety metrics through simulation experiments.
Expertise may be gained during a doctoral program.

Responsibilities

Build and maintain large-scale machine learning infrastructure, ML, data, and application pipelines; evaluate ML models.
Develop sophisticated analytics, machine-learning platforms, and software for model prediction and optimization.
Develop scalable distributed systems and software applications using open-source technologies.
Extend existing ML platforms and frameworks to scale model training and deployment.
Partner with business and software engineering teams to drive adoption and integration of model outputs.
Implement big-data solutions in the data analytics space.
Plan, create, and maintain data architectures aligned with business requirements.
Build data-flow channels and processing systems to ETL and integrate data from multiple sources.
Consult with systems analysts and software engineers/developers to design systems and understand project limitations, capabilities, performance requirements, and interfaces.
Develop software system testing and validation procedures, programming, and documentation.
Advise senior management on technical strategy and mentor junior team members.
Perform independent, complex technical and functional analysis for multiple projects supporting divisional initiatives.
Plan and coordinate complex project schedules and assignments, and meet project goals on time and within budget.
Architect, design, and build scalable big-data frameworks and analytics platforms using Apache Spark and Kubernetes.
Develop and optimize EMR workloads on EKS for petabyte-scale datasets, and improve scalability and performance through testing and analysis using AWS services.
Design, implement, and maintain CI/CD pipelines for distributed systems; automate deployment workflows and monitoring; and migrate on-premises platforms to AWS.
Conduct AI and ML research focused on reinforcement learning; develop algorithms using Monte Carlo Tree Search for decision-making in multi-agent systems; and implement and validate RL models in simulation.
Design and develop real-time simulation platforms; implement multi-agent RL models in large-scale simulations; and evaluate model performance and safety metrics through experiments.