Note: The job is a remote job and is open to candidates in USA. ELLKAY is hiring a Staff Machine Learning Engineer - Agentic AI to architect, build, and deploy production-grade AI/ML and agentic systems on AWS. The role focuses on agentic and foundation model applications, healthcare data standards, secure cloud infrastructure, and evaluation of AI systems.
Responsibilities
- Architect, implement, and deploy end-to-end Agentic AI systems utilizing tool calling, stateful multi-agent collaboration, dynamic task routing, reflection loops, and self-correction workflows using frameworks like LangGraph, AutoGen, CrewAI, or Bedrock Agents
- Design and implement production foundation model applications using advanced prompt engineering, structured output design, function-calling patterns, dynamic tool retrieval, and hallucination mitigation strategies
- Build and optimize vector search and long-term memory 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) for RAG and agent context retrieval
- Develop and deploy Amazon Bedrock and Bedrock Agent 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 and agent tool interfaces 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) across LLM inputs, tool payloads, and agent memory states
- Build high-performance APIs and agent execution backends using FastAPI and Pydantic v2 with async I/O patterns, robust retry/backoff mechanisms, state management, 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 for agent microservices on ECS Fargate with auto-scaling policies, service discovery, and Fargate Spot optimization
- Architect data and state 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, agent execution trajectory tracing, structured JSON logging, and correlation ID propagation
- Apply security engineering best practices including IAM least-privilege roles for tool executions, KMS envelope encryption, Secrets Manager rotation, JWT validation, tenant isolation via session tags, and OWASP API Top-10 awareness
- Design rigorous evaluation frameworks for foundation models and AI agents, including tool selection precision, execution success rates, benchmark creation, per-class metrics, held-out test sets, label leakage prevention, and statistical significance testing
- Implement prompt and agent strategy versioning, human-in-the-loop evaluation pipelines, and A/B testing frameworks to continuously improve performance
Skills
- Proficiency with Pydantic v2, Pandas/Polars, FastAPI, Boto3, Asyncio, and Agent/LLM frameworks (e.g., LangGraph, AutoGen, LlamaIndex)
- Proven experience designing stateful agents, function/tool calling interfaces, autonomous reasoning loops, structured output generation, and evaluation harnesses
- 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 for RAG and agent memory stores
- Experience with scikit-learn, clustering algorithms, and confidence calibration techniques
- Good understanding of IAM, KMS, secrets management, and OWASP security principles
- 10+ years of software engineering experience with at least 3 years focused on production AI/ML and GenAI/Agentic systems
- Proven track record of deploying production ML and agentic applications at scale
- Demonstrated ability to design and implement end-to-end ML and agent evaluation frameworks
- Well versed in AI development leveraging tools such as Codex/Claude Code
- Working knowledge of FHIR R4, HL7 v2, USCDI, LOINC, SNOMED, and RxNorm
- Experience with healthcare data or regulated industries
Benefits
- Medical, Dental, and Vision benefits
- Employer-paid Life and LTD
- 401k w/ matching – once eligibility is met
- Work/life balance
- Paid Volunteer Program
- Flexible working hours
- Generous FTO
- Remote work options
- Employee Discounts
- Parental Leave
- Gym membership / Exercise class stipends
- Working with talented, collaborative, and friendly people who love what they do
- Professional growth within
- Innovation environment
- Free daily lunches on site in HQ
- Hybrid workplace, with flexibility in how employees structure their time between in-office and remote work
- For those who do not live within 40 miles of one of our offices, remote work may be considered for candidates whose skills and experience strongly align with the role
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