Staff Machine Learning Engineer

ELLKAY, LLC• US,

12h•Hybrid

About The Position

We're looking for a versatile Full Stack AI/ML Engineer who can architect, build, and deploy production-grade AI/ML systems on AWS infrastructure. This role combines deep expertise in foundation models, healthcare data standards, and serverless cloud architecture to create scalable, secure, and compliant AI solutions for healthcare organizations.

Requirements

Python Stack: Proficiency with pydantic v2, pandas/polars, fastapi, boto3, asyncio, and prompt-template libraries
Foundation Models: Hands-on experience with prompt engineering, structured output generation, and evaluation harnesses for LLMs
AWS Services: Production experience with Lambda, API Gateway, Step Functions, EventBridge, SQS, SNS, ECS Fargate, Aurora PostgreSQL, DynamoDB, S3, and AWS CDK
Vector Search: Practical knowledge of OpenSearch Serverless, k-NN algorithms, hybrid retrieval, and ranking optimization
Machine Learning: Experience with scikit-learn, clustering algorithms, and confidence calibration techniques
Healthcare Standards (preferable) : Working knowledge of FHIR R4, HL7 v2, USCDI, LOINC, SNOMED, and RxNorm
Security: Strong understanding of IAM, KMS, secrets management, and OWASP security principles
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
Strong problem-solving abilities and attention to detail
Excellent communication skills for collaborating with cross-functional teams
Self-motivated with ability to work independently and drive projects to completion
Commitment to code quality, testing, and engineering best practices

Nice To Haves

Experience with Amazon Bedrock and Claude family models
Familiarity with HIPAA compliance requirements and healthcare security standards
Contributions to open-source ML or healthcare interoperability projects
Degree in Computer Science, Machine Learning, or related field

Responsibilities

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