高级人工智能/机器学习工程师，应用与自动化

We are seeking a Senior AI/ML Engineer to design, build, deploy, and evolve AI models, agents, and workflow automation for clinical terminology and content operations. This hands-on role combines AI/ML development with production ownership, taking models and agents from experimentation to reliable production use. The ideal candidate has experience with large language models, agent frameworks, retrieval-augmented generation, and the infrastructure and controls required to operate AI systems reliably.
WHAT YOU’LL DO:

• Develop machine learning models, agents, and automation workflows for terminology management, content creation, mapping, and validation — evolving them from experimentation into scalable production systems.

• Build agentic workflows that use LLMs, tools, APIs, knowledge sources, retrieval capabilities, and structured business rules to complete complex tasks.

• Build and maintain retrieval-augmented generation solutions, vector and semantic search capabilities, and prompt and context-management strategies.

• Partner with our data science team to understand, integrate, and productionize their existing agents, and bring your own model and agent development to the team's roadmap.

• Own the deployment, monitoring, troubleshooting, and continuous improvement of AI workflows in production, including root-cause analysis and durable remediation of failures or unexpected outputs.

• Design evaluation, testing, and observability practices for AI systems, and implement controls for auditability, explainability, and human-in-the-loop review in clinically sensitive workflows.

• Develop cloud-based solutions using AWS services such as Amazon Bedrock, SageMaker, and Lambda, applying CI/CD, containerization, automated testing, and secure development practices.

• Work closely with clinical, mapping, product, data science, and engineering partners to translate workflows into practical solutions — and help define where AI automation is appropriate, where deterministic logic is required, and where human review must remain.

WHAT YOU’LL NEED:

• 5+ years across AI/ML engineering, data science, machine learning engineering, or related disciplines, with a foundation in applied machine learning.

• Hands-on experience building agents and agentic workflows, including orchestration and tool or function calling.

• Hands-on experience building RAG solutions, including embeddings, vector databases, semantic search, and context engineering.

• Hands-on MLOps experience taking models and agents into production — deployment, versioning, monitoring, and CI/CD across multiple environments.

• Strong Python proficiency and experience developing maintainable services, APIs, pipelines, or workflow automation, plus working knowledge of SQL and relational databases such as PostgreSQL.

• Experience with cloud-based AI infrastructure, preferably AWS and Amazon Bedrock.

• Strong troubleshooting and root-cause analysis skills, and the ability to partner with domain experts and convert ambiguous workflow needs into scalable technical solutions.

• Clear written and verbal communication in cross-functional environments.

PREFERRED QUALIFICATIONS:

• LangChain or LangGraph, LlamaIndex, OpenSearch, vector databases, or evaluation frameworks.

• Multi-agent or tool-using workflows, including state management, memory, routing, and failure recovery.

• Testing and evaluation approaches for non-deterministic AI systems.

• Healthcare technology, clinical terminology, clinical data normalization, mapping workflows, or regulated data environments.

• Familiarity with healthcare data standards such as knowledge graphs, FHIR, SNOMED CT, LOINC, RxNorm, ICD-10, or CPT.

• AI solutions incorporating human review, auditability, explainability, and quality governance.

Originally posted on Himalayas