Senior Machine Learning Engineer
ELLKAY seeks a Senior Machine Learning Engineer to architect, build, and deploy production AI/ML systems on AWS for healthcare: foundation-model apps (Bedrock/Claude), vector search on OpenSearch, serverless Python backends (FastAPI, Lambda, CDK), all compliant with healthcare data standards like FHIR, HL7, and clinical terminologies.
- Compensation
- Not disclosed
- Location
- IN
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- 8+ years software engineering experience, with at least 3 years focused on AI/ML systems
- Proficiency with pydantic v2, pandas/polars, fastapi, boto3, asyncio, and prompt-template libraries
- Hands-on experience with prompt engineering, structured output generation, and evaluation harnesses for LLMs
- Production experience with Lambda, API Gateway, Step Functions, EventBridge, SQS, SNS, ECS Fargate, Aurora PostgreSQL, DynamoDB, S3, and AWS CDK
- Practical knowledge of OpenSearch Serverless, k-NN algorithms, hybrid retrieval, and ranking optimization
- Experience with scikit-learn, clustering algorithms, and confidence calibration techniques
- Strong understanding of IAM, KMS, secrets management, and OWASP security principles
- Proven track record of deploying production ML systems at scale
- 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 have
- Working knowledge of FHIR R4, HL7 v2, USCDI, LOINC, SNOMED, and RxNorm
- Experience with healthcare data or regulated industries
- 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
Full description
ELLKAY started out providing connectivity solutions to laboratories and within a few years, grew to also provide data management solutions to ambulatory organizations. ELLKAY is now a trusted data management partner in five healthcare segments. ELLKAY’s solutions continue to serve laboratories and ambulatory practices and have expanded to empower hospitals and health systems, healthcare IT vendors, ambulatory practices, health plans, and other healthcare organizations with cutting-edge technologies and solutions that drive their growth and interoperability strategies.
Today, ELLKAY remains true to our core values, building strong partner relationships and offering unparalleled service and support while providing innovative, scalable solutions to the challenges our customers face in today’s data-rich world.
ELLKAY's experience, customer-focused approach, and reputation for innovation, speed, and accuracy differentiate ELLKAY as a premier partner for your interoperability needs and data management strategy.
Job Description:
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.
Essential Duties & Responsibilities:
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 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
Healthcare Data & 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 normalization
- Ensure HIPAA compliance and implement appropriate security controls for protected health information (PHI)
Backend & Infrastructure
- Build high-performance APIs using FastAPI and Pydantic v2 with async I/O patterns, robust retry/backoff mechanisms, and structured error handling