Sr. ML Engineer

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 organisations.

The candidate will have responsibilities across the following functions:

AI/ML Engineering:

• 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 optimise 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 optimising 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.

Healthcare Data and Standards:

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

Backend and Infrastructure:

• 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 optimisation.
• 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.

Observability and Security:

• 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.

Evaluation and Quality:

• 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.

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 optimisation.
• Machine Learning: Experience with scikit-learn, clustering algorithms, and confidence calibration techniques.
• Healthcare Standards (preferred): Working knowledge of FHIR R4, HL7 v2, USCDI, LOINC, SNOMED, and RxNorm.
• Security: Strong understanding of IAM, KMS, secrets management, and OWASP security principles.

Experience:

• 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.

Soft Skills:

• Strong problem-solving abilities and attention to detail.
• Excellent communication skills for collaborating with cross-functional teams.
• Self-motivated with the ability to work independently and drive projects to completion.
• Commitment to code quality, testing, and engineering best practices.

Skills

AWS, Amazon Web Services, LLM, ML, Machine Learning, Python