Overview
On Site
BASED ON EXPERIENCE
Contract - Independent
Contract - W2
Skills
Requirements Elicitation
Legal
Regulatory Compliance
Agile
Sprint
Estimating
Modeling
Privacy
Optimization
Artificial Intelligence
Large Language Models (LLMs)
Training
Continuous Integration
Continuous Delivery
Git
Scalability
DevOps
Machine Learning Operations (ML Ops)
Interfaces
Real-time
UI
Usability
User Experience
Systems Architecture
Workflow
Management
Amazon SageMaker
Testing
Machine Learning (ML)
Collaboration
Quality Assurance
Amazon Web Services
Root Cause Analysis
Computer Science
Job Details
Job Title: AI/ML Engineer
Location: Malvern, PA (onsite day 1 for hybrid model)
Length: 1+ year (likely to be extended multiple years)
1. Requirement Gathering & Collaboration
Time Allocation: 10%
Time Allocation: 55%
Time Allocation: 15%
Time Allocation: 10%
Time Allocation: 10%
Location: Malvern, PA (onsite day 1 for hybrid model)
Length: 1+ year (likely to be extended multiple years)
Job Responsibilities
1. Requirement Gathering & Collaboration
Time Allocation: 10%
- Collaborate with data scientists and stakeholders in legal and compliance to define machine learning solution requirements.
- Participate in Agile ceremonies such as sprint planning, backlog grooming, and feature estimation.
- Translate business needs into technical tasks related to data ingestion, modeling, deployment, and UI development.
- Ensure all technical implementations comply with internal security, privacy, and CI/CD standards.
Time Allocation: 55%
- Build and deploy AI/ML models using AWS SageMaker, including fine-tuning and inference pipelines for Large Language Models (LLMs).
- Design and maintain scalable data pipelines for model training and inference (real-time and batch).
- Integrate models into internal systems with robust logging, monitoring, and exception handling.
- Implement CI/CD pipelines using AWS services and Git workflows to automate testing and deployment.
- Optimize model deployments for scalability, security, and cost-efficiency following DevOps/MLOps best practices.
Time Allocation: 15%
- Develop responsive user interfaces using Streamlit to enable user interaction with ML models and tools.
- Connect UIs to backend inference APIs or SageMaker endpoints for real-time feedback.
- Enhance UI usability, performance, and error handling to ensure a seamless user experience.
Time Allocation: 10%
- Maintain and improve internal LLM tools based on user feedback and evolving model capabilities.
- Monitor model performance and drift; initiate retraining workflows as needed.
- Document system architecture, workflows, and updates to ensure operational continuity.
- Manage model versions and experiments using SageMaker Model Registry.
Time Allocation: 10%
- Develop unit, integration, and functional tests for ML models, data pipelines, and UIs.
- Collaborate with QA and Data teams to ensure comprehensive test coverage.
- Implement monitoring and alerting using AWS CloudWatch and related tools to maintain system health.
- Provide post-deployment support, troubleshoot issues, and perform root cause analysis.
- Bachelor's degree in Computer Science or a related field.
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