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 cutting-edge, 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.

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.

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)

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

Backend & 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 + 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

Evaluation & 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

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

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

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

Self-motivated with ability to work independently and drive projects to completion

Commitment to code quality, testing, and engineering best practices

Experience with Amazon Bedrock and Claude family models

Contributions to open-source ML or healthcare interoperability projects

Degree in Computer Science, Machine Learning, or related field

ELLKAY is committed to fostering a cooperative and high-performance work environment that supports innovation, teamwork, and professional growth. Most roles are designed to operate from our office locations to encourage effective collaboration and engagement across teams.

ELLKAY, LLC provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

📌 Senior Machine Learning Engineer - AI (India)
🏢 ELLKAY
📍 India