Resolve Tech Solutions
Position Summary
The AI/ML Engineer (Bedrock) supports the Juno Labs team by developing, evaluating, and operationalizing AI-driven solutions for anomaly detection, alert summarization, and automated resolution recommendations. This role combines strong applied machine learning skills with hands-on experience in model deployment and productionization, leveraging AWS Bedrock, SageMaker, and other modern AI/ML frameworks.
The engineer will design and refine prompt-based workflows, implement classic and deep learning models, and collaborate closely with cloud, data, and security teams to ensure solutions are scalable, secure, and aligned with enterprise objectives.
Key Responsibilities
Develop and optimize prompt-based workflows for anomaly detection to identify irregular patterns in system performance and data flow
Design and implement summarization prompts to generate concise, actionable alert descriptions for operations teams
Build, evaluate, and fine‑tune AI/ML models—including classic ML and neural architectures—to support anomaly detection and resolution recommendation use cases
Lead deployment and operationalization of AI/ML models into production environments, ensuring scalability, security, and performance monitoring
Utilize platforms such as AWS SageMaker, TensorFlow, and PyTorch for model training, inference, and lifecycle management
Apply Scikit‑Learn for traditional machine learning methods, including gradient boosting decision trees (GBDT, XGBoost), and compare model performance across techniques
Collaborate with CloudOps, DataOps, and Security teams to integrate AI‑driven workflows into enterprise monitoring and incident response pipelines
Validate and continuously evaluate model performance, maintaining accuracy, reliability, and compliance with data governance standards
Monitor real‑time operational data streams to refine detection thresholds and enhance anomaly identification over time
Document architecture, data flows, and best practices to support reproducibility and continuous improvement
Experiment with new features and capabilities in AWS Bedrock and related LLM‑based frameworks to enhance prompt engineering and workflow automation
Ensure adherence to RTS security, compliance, and quality standards throughout the development and deployment lifecycle
Other duties as assigned
Qualifications
Bachelor’s degree in Computer Science, Information Technology, or related field, or equivalent experience
3+ years of experience in AI/ML engineering or applied machine learning roles
Hands‑on experience with AWS Bedrock and SageMaker or equivalent LLM/AI platforms
Proficiency with TensorFlow or PyTorch for deep learning model development
Experience with Scikit‑Learn and GBDT/XGBoost for traditional ML tasks
Strong understanding of model evaluation, deployment, and operationalization best practices
Familiarity with anomaly detection, summarization, and recommendation system design
Experience integrating AI solutions into DevOps, CloudOps, or monitoring pipelines
Excellent collaboration, problem‑solving, and documentation skills
Alignment with RTS Core Values
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Consulting
Industries
IT Services and IT Consulting
Benefits
Medical insurance
Vision insurance
401(k)
Paid maternity leave
Paid paternity leave
Disability insurance
Location: Austin, TX
#J-18808-Ljbffr
The engineer will design and refine prompt-based workflows, implement classic and deep learning models, and collaborate closely with cloud, data, and security teams to ensure solutions are scalable, secure, and aligned with enterprise objectives.
Key Responsibilities
Develop and optimize prompt-based workflows for anomaly detection to identify irregular patterns in system performance and data flow
Design and implement summarization prompts to generate concise, actionable alert descriptions for operations teams
Build, evaluate, and fine‑tune AI/ML models—including classic ML and neural architectures—to support anomaly detection and resolution recommendation use cases
Lead deployment and operationalization of AI/ML models into production environments, ensuring scalability, security, and performance monitoring
Utilize platforms such as AWS SageMaker, TensorFlow, and PyTorch for model training, inference, and lifecycle management
Apply Scikit‑Learn for traditional machine learning methods, including gradient boosting decision trees (GBDT, XGBoost), and compare model performance across techniques
Collaborate with CloudOps, DataOps, and Security teams to integrate AI‑driven workflows into enterprise monitoring and incident response pipelines
Validate and continuously evaluate model performance, maintaining accuracy, reliability, and compliance with data governance standards
Monitor real‑time operational data streams to refine detection thresholds and enhance anomaly identification over time
Document architecture, data flows, and best practices to support reproducibility and continuous improvement
Experiment with new features and capabilities in AWS Bedrock and related LLM‑based frameworks to enhance prompt engineering and workflow automation
Ensure adherence to RTS security, compliance, and quality standards throughout the development and deployment lifecycle
Other duties as assigned
Qualifications
Bachelor’s degree in Computer Science, Information Technology, or related field, or equivalent experience
3+ years of experience in AI/ML engineering or applied machine learning roles
Hands‑on experience with AWS Bedrock and SageMaker or equivalent LLM/AI platforms
Proficiency with TensorFlow or PyTorch for deep learning model development
Experience with Scikit‑Learn and GBDT/XGBoost for traditional ML tasks
Strong understanding of model evaluation, deployment, and operationalization best practices
Familiarity with anomaly detection, summarization, and recommendation system design
Experience integrating AI solutions into DevOps, CloudOps, or monitoring pipelines
Excellent collaboration, problem‑solving, and documentation skills
Alignment with RTS Core Values
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Consulting
Industries
IT Services and IT Consulting
Benefits
Medical insurance
Vision insurance
401(k)
Paid maternity leave
Paid paternity leave
Disability insurance
Location: Austin, TX
#J-18808-Ljbffr