AI/ML Engineer

Key Responsibilities

• Configure and optimize Amazon Bedrock Agents for virtual agent fulfillment, real-time coaching, call summarization, and post-call quality analysis
• Design, version-control, and iteratively refine prompt templates for each AI capability, measuring performance against SLA accuracy targets (≥95% QA match)
• Build and maintain the AWS Bedrock Knowledge Base, including content ingestion pipelines synced from Salesforce and semantic retrieval optimization using Amazon Nova models
• Develop coaching rulesets that map required disclosures, HIPAA-sensitive language patterns, and DHA-mandated scripts to specific call types for real-time compliance monitoring
• Implement QA scoring rubrics for the post-call analysis platform, enabling automated evaluation of sentiment, compliance, accuracy, and resolution effectiveness across 100% of calls
• Configure Bedrock Guardrails for PHI/PII redaction, hallucination detection, prompt-injection protection, and content filtering on every inference call
• Collaborate with the Integration Engineer to ensure Bedrock inference outputs are properly formatted for delivery to Salesforce (via Lambda and Platform Events) and to Verint Monitor model performance using CloudWatch metrics and QuickSight dashboards; identify and remediate model drift at Day 30 and Day 60 re-benchmarking intervals
• Participate in biweekly sprint demos, presenting AI performance data, prompt tuning results, and accuracy metrics to TriWest stakeholders
• Work with TriWest QA reviewers and SMEs to close knowledge gaps identified through post-call analysis feedback loops
• Ensure all AI inference and logging complies with FedRAMP High, DFARS 252.204-7012, HIPAA, and NIST AI RMF requirements

Required Qualifications

• 3+ years of experience in applied AI/ML engineering, with hands-on work building LLM-based applications (prompt engineering, RAG architectures, agent frameworks)
• Direct experience with Amazon Bedrock, including Agent configuration, Knowledge Base setup, Guardrails, and foundation model invocation
• Proficiency with Amazon Nova model family or equivalent foundation models (e.g., Claude, Titan) in production environments
• Strong Python and/or Node.js skills for Lambda function development and AI pipeline integration
• Familiarity with AWS serverless services: Lambda, Kinesis, S3, CloudWatch, and IAM within GovCloud environments
• Understanding of healthcare data privacy requirements (HIPAA, PHI/PII handling) and federal compliance frameworks (FedRAMP, NIST 800-53)
• Experience with version-controlled prompt management and A/B testing of prompt variants
• Strong analytical skills for interpreting model performance metrics and translating findings into prompt or architecture improvements
• Bachelor’s degree in Computer Science, Data Science, AI/ML, or related field (or equivalent experience)

Preferred Qualifications

• Experience with Amazon Connect, Contact Lens, and contact center AI workflows
• Prior work in FedRAMP High or IL4/IL5 environments
• Familiarity with Salesforce integration patterns (Platform Events, Streaming API, REST/SOAP APIs)
• AWS certifications: Machine Learning Specialty, Solutions Architect, or DevOps Professional
• Experience with TRICARE, DHA, or healthcare insurance domain knowledge
• Background in conversational AI design (IVR, chatbots, voice agents)