Associate Data Engineer (AIML)

Why Ryan

• Hybrid Work Options

• Award-Winning Culture

• Generous Personal Time Off (PTO) Benefits

• 14-Weeks of 100% Paid Leave for New Parents (Adoption Included)

• Monthly Gym Membership Reimbursement OR Gym Equipment Reimbursement

• Benefits Eligibility Effective Day One

• 401K with Employer Match

• Tuition Reimbursement After One Year of Service

• Fertility Assistance Program

• Four-Week Company-Paid Sabbatical Eligibility After Five Years of Service

Duties and Responsibilities aligned with Key Results:

People

• Use a variety of programming languages and tools to develop test and maintain data pipelines within the Platform Reference Architecture.
• Working directly with management product teams and practice personnel to understand their platform data requirements
• Maintaining a positive work atmosphere by behaving and communicating in a manner that encourages productive interactions with customers co-workers and supervisors
• Developing and engaging with team members by creating a motivating work environment that recognizes holds team members accountable and rewards strong performance
• Fostering an innovative inclusive and diverse team environment promoting positive team culture encouraging collaboration and self-organization while delivering high quality solutions

Client

• Collaborating on an Agile team to design develop test implement and support highly scalable data solutions
• Collaborating with product teams and clients to deliver robust cloud-based data solutions that drive tax decisions and provide powerful experiences
• Analyzing user feedback and activity and iterate to improve the services and user experience

Value

• AI/ML Model Development: Design build and optimize machine learning models and AI solutions using techniques such as supervised/unsupervised learning deep learning natural language processing (NLP) and computer vision. Use frameworks such as TensorFlow PyTorch Keras XGBoost Scikit-learn and MLFlow. NLP experience includes NLTK BERT GPT. Ensure the models can be deployed and scaled in cloud environments like Azure ML Azure Document Intelligence Databricks and/or AWS SageMaker.
• Data Apps Development: Have solid understanding in software best practices for flexible extensible microservice application architectures that reside in an overall dynamic distributed system. Understand how to implement resilient robust production-grade code that runs in a cloud environment in container services like AKS EKS ECS Container Apps in Azure and/or AWS and interfaces with other cloud and application services.
• End-to-End AI/ML Pipeline: Develop and maintain scalable AI/ML pipelines from data ingestion and preprocessing to model training validation and deployment using MLOps best practices. Have hands-on experience with technologies like MLFlow.
• Generative AI Applications: Apply GenAI techniques to real-world business cases developing models that generate data-driven insights automate processes and enhance operational efficiency. Apply agentic flows and leverage RAG solutions where appropriate. Have a firm understanding of similarity / RAG basic and advanced patterns and agentic flows. Be hands-on with fine-tuning and libraries like Langchain Ollama Llamaindex Langroid CrewAI VanniAI. Understand and have experience with Vector DBs like Milvus OpenSearch Azure AI Search and PGVector.
• AI/ML Solution Deployment: Deploy machine learning and AI models in production environments using cloud platforms like AWS Azure using SageMaker Azure Document Intelligence Azure ML and/or Databricks ensuring robust integration with existing systems. Understand cost savings trade-offs for training and inference and leveraging serverless soutions.
• AI Innovation & Application: Work on cutting-edge AI initiatives including generative AI reinforcement learning and neural network architectures (e.g. CNNs RNNs Transformers) applying them to real-world use cases.
• Model Monitoring & Optimization: Implement model monitoring performance tracking and continuous improvement strategies ensuring that AI/ML models maintain accuracy performance and scalability over time. Understand how to handle model and data drift.
• Collaboration with Cross-Functional Teams: Partner with data scientists data engineers software developers and product managers to understand business objectives define AI/ML solutions and deliver impactful results.
• AI/ML Infrastructure & Automation: Build infrastructure for deploying and managing machine learning models at scale incorporating MLOps practices to automate deployment monitoring and retraining in Databricks using MLFlow.

Education and Experience:

• Bachelors and/or masters degree in Computer Science Data Science Engineering or a related field.
• Recent graduate or candidate with internship academic project research or hands-on experience developing data and software solutions.
• Foundational understanding of ETL processes and data integration concepts through coursework internships or personal projects.
• Exposure to cloud-based data services and platforms such as AWS or Azure is preferred.
• Familiarity with programming languages such as Python Scala or similar technologies through academic or project experience.
• Basic understanding of Windows and Linux environments and willingness to learn in a mixed-platform setting.

Computer Skills:

To perform this job successfully an individual must have intermediate knowledge of Microsoft Project Word Excel Access PowerPoint Outlook and Internet navigation and research

Certificates and Licenses:

Valid drivers license required.

Supervisory Responsibilities:

None

Work Environment:

• Standard indoor working environment.
• Occasional long periods of sitting while working at computer.
• Must be able to lift carry push or pull up to 30 lbs.
• Position requires regular interaction with employees at all levels of the Firm and interface with external vendors as necessary.
• Independent travel requirement: As Needed

Equal Opportunity Employer: disability/veteran