Fidelity will not provide immigration sponsorship for this position.Job Description:Position Description:
Builds and maintains large scale Machine Learning (ML) infrastructure, ML, data, and application pipelines, and evaluates ML models, using Apache Spark and distributed data processing frameworks. Establishes Python ML ecosystem using Object Oriented Programming (Java, Scala, and Python), SQL, and Unix scripting. Leverages data movement technologies (Extract, Transform, Load (ETL)/ Extract, Load, Transform (ELT)), messaging and streaming technologies (AWS SQS, Kinesis, and Kafka), relational and NoSQL databases (DynamoDB, EKS, and Graph databases), and Application Programming Interface (API) and in-memory technologies. Develops highly scalable distributed systems using Open-source technologies and Continuous Integration/Continuous Delivery (CI/CD) tools. Uses business knowledge to translate the vision for divisional initiatives into business solutions by developing complex ML infrastructure and conducting studies of alternatives. Analyzes and recommends changes in project development policies, procedures, standards, and strategies to development experts and management.
Primary Responsibilities:- Builds sophisticated analytics, ML platforms, and software to enable moderl prediction and optimization.
- Develops highly scalable distributed systems and software applications using open-source technologies.
- Extends existing ML platforms and frameworks for scaling model training and deployment.
- Partners closely with business and software engineering teams to drive the adoption and integration of model outputs.
- Implements big data solutions in the data analytics space.
- Plans, creates, and maintains data architectures that are fully align with business requirements.
- Builds data flow channels and processing systems to ETL and integrate data from various sources.
- Confers with systems analysts and software engineers/developers to design systems and obtain information on project limitations and capabilities, performance requirements, and interfaces.
- Develops software system testing and validation procedures, programming, and documentation.
- Advises senior management on technical strategy.
- Mentors junior team members.
- Performs independent and complex technical and functional analysis for multiple projects supporting several divisional initiatives.
- Plans and coordinates complex project schedules and assignments for multiple projects.
- Responsible for meeting project goals on-time and on-budget.
Education and Experience:
Bachelor's degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and five (5) years of experience as a Principal AI/ML Engineer (or closely related occupation) 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.
Or, alternatively, Master's degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Principal AI/ML Engineer (or closely related occupation) 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.
Skills and Knowledge:
Candidate must also possess:
- Demonstrated Expertise architecting, designing, and building scalable big data frameworks and analytics platforms, using Apache Spark and Kubernetes; developing and optimizing EMR workloads on EKS to support petabyte-scale datasets; and increasing platform scalability and performance by conducting testing and analysis, 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, using AWS CloudWatch; and performing platform migration from on-premises systems to AWS Cloud infrastructure, ensuring scalability and reliability in a production environment.
- Demonstrated Expertise conducting advanced research in AI and ML, focusing on Reinforcement Learning (RL); developing novel AI algorithms using advanced RL methods -- Monte Carlo Tree Search for decision making in multi-agent systems environment; and implementing and validating RL models in a simulation environment, using Java, Python, and JMonkey Game Engine to improve safety and scalability.
- Demonstrated Expertise designing and developing real-time simulation platforms for testing and validating novel AI algorithms; implementing multi-agent RL models in large-scale simulations using Java-based 3D engines; and running simulation experiments to evaluate model performance and safety metrics, contributing to peer-reviewed publications and advancing multi-agent autonomous systems research.
[Expertise may be gained during doctoral program.]
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Fidelity's Onsite Working Model
Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.
Certifications:Category:Information Technology
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