Staff Machine Learning Engineer

Design and implement production foundation model applications using prompt engineering techniques including structured output design, few-shot learning, function-calling patterns, and hallucination mitigation strategies

Build and optimize vector search solutions using OpenSearch Serverless with k-NN, including HNSW/IVF parameter tuning, hybrid retrieval (lexical + semantic), and multi-stage ranking pipelines (BM25 + dense + re-ranking)

Develop and deploy Amazon Bedrock solutions leveraging Claude family models, Titan Embeddings v2, and Guardrails for PHI/PII detection while optimizing for provisioned vs on-demand throughput

Implement classical ML pipelines including clustering algorithms (HDBSCAN, k-means), confidence score calibration (Platt scaling, isotonic regression), and model evaluation frameworks

Design and implement systems that work with FHIR R4 resources, HL7 v2 message structures, and USCDI data elements

Build ontology retrieval systems against LOINC, SNOMED, and RxNorm terminologies to support clinical decision-making and data normalization

Ensure HIPAA compliance and implement appropriate security controls for protected health information (PHI)

Build high-performance APIs using FastAPI and Pydantic v2 with async I/O patterns, robust retry/backoff mechanisms, and structured error handling

Design and deploy AWS serverless architectures including Lambda (Python + container), API Gateway (REST + HTTP), Step Functions, EventBridge, SQS, and SNS

Implement container-based workloads on ECS Fargate with auto-scaling policies, service discovery, and Fargate Spot optimization

Architect data solutions using Aurora PostgreSQL Serverless v2, DynamoDB, and S3 with appropriate indexing, partitioning, caching, and lifecycle policies

Write production-grade Infrastructure as Code using AWS CDK (TypeScript or Python) supporting multi-tenant provisioning and cross-account deployments

Implement comprehensive observability using AWS X-Ray distributed tracing, CloudWatch metrics/logs/alarms, structured JSON logging, and correlation ID propagation

Apply security engineering best practices including IAM least-privilege roles, KMS envelope encryption, Secrets Manager rotation, JWT validation, tenant isolation via session tags, and OWASP API Top-10 awareness

Design rigorous evaluation frameworks including benchmark creation, per-class F1/precision/recall metrics, held-out test sets, label leakage prevention, and statistical significance testing for model comparisons

Implement prompt versioning and A/B testing frameworks to continuously improve model performance

Qualifications:

8+ years of software engineering experience with at least 3 years focused on AI/ML systems

Proven track record of deploying production ML systems at scale

Experience with healthcare data or regulated industries strongly preferred

Demonstrated ability to design and implement end-to-end ML evaluation frameworks