Senior AI Engineer

We are seeking a highly skilled and innovative Senior Software Engineer Agentic AI Engineer to join AIS Digital Imaging360. The role will focus on designing developing and deploying production-grade AI solutions that leverage traditional AI/ML Generative AI Retrieval-Augmented Generation knowledge graphs and agentic workflows to solve complex healthcare imaging problems. The ideal candidate will bring 10 years of software engineering experience strong AI engineering depth and a platform-agnostic mindset with the ability to evaluate and implement solutions across cloud open-source and enterprise AI ecosystems.

In this role you will:

• Design build and deploy agentic AI systems that support autonomous reasoning planning tool use multi-step execution and human-in-the-loop workflows for Imaging360 use cases.
• Develop scalable AI/ML and GenAI solutions across the full lifecycle including data ingestion feature engineering model experimentation evaluation deployment monitoring and continuous improvement.
• Architect and implement Retrieval-Augmented Generation solutions that combine structured and unstructured healthcare product operational and workflow data with strong grounding relevance and traceability.
• Design knowledge graph and GraphRAG solutions that model relationships across imaging assets clinical entities devices and workflows combining graph traversal with vector retrieval for multi-hop reasoning and explainable grounding.
• Engineer evaluation and testing harnesses for agentic systems including automated eval pipelines golden datasets LLM-as-judge scoring regression suites for prompts and agents simulation-based agent testing and red-teaming for adversarial robustness.
• Optimize token usage latency and inference cost through context engineering prompt compression prompt and semantic caching model routing between frontier and small language models batching and structured output constraints.
• Build platform-agnostic AI services using appropriate cloud-native open-source or enterprise AI capabilities while avoiding dependency on a single AI vendor or model provider.
• Integrate LLMs embedding models orchestration frameworks vector stores APIs and backend services into secure reliable and maintainable production applications.
• Create rapid proofs of concept for emerging AI GenAI and agentic patterns; harden successful prototypes into reusable production components and engineering patterns.
• Collaborate with product managers architects UX data engineering cybersecurity quality regulatory cloud operations and scrum teams to translate Imaging360 business needs into responsible AI solutions.
• Define and implement AI evaluation methods for accuracy relevance grounding robustness latency cost safety fairness explainability and operational reliability.
• Drive engineering excellence through clean architecture API design automated testing CI/CD documentation code reviews design reviews and production readiness practices.
• Ensure AI solutions meet healthcare-grade expectations for privacy security auditability data governance compliance and responsible AI adoption.

• Programming and software engineering: Strong hands-on experience with Python and modern backend engineering; exposure to Java TypeScript or similar languages is preferred.
• AI/ML and GenAI: Experience with machine learning deep learning NLP LLM application development embeddings prompt engineering fine-tuning or parameter-efficient tuning and model evaluation.
• Agentic AI: Experience designing AI agents multi-agent workflows tool orchestration reasoning/planning loops function calling agent memory guardrails and workflow automation.
• RAG and knowledge systems: Experience building retrieval pipelines indexing strategies chunking reranking metadata filtering vector databases hybrid search and grounded response generation.
• Knowledge graphs: Experience with graph databases (Neo4j Amazon Neptune or equivalent) ontology and entity modeling entity resolution GraphRAG patterns and hybrid graph plus vector retrieval.
• Agent evaluation and harness engineering: Experience with evaluation frameworks (Ragas DeepEval promptfoo LangSmith Langfuse or equivalent) agent trajectory evaluation offline and online eval loops A/B testing and CI-integrated evaluation gates.
• Cost and performance optimization: Token accounting and budgeting prompt caching KV-cache-aware design model right-sizing and routing quantization and distillation awareness and cost observability per request and workflow.
• Interoperability protocols: Working knowledge of Model Context Protocol (MCP) for tool and data integration and agent-to-agent (A2A) communication patterns; experience building or consuming MCP servers is a plus.
• Multimodal AI: Experience with vision-language models for medical imaging context document understanding (reports scanned forms) and multimodal RAG.
• Architecture and integration: Strong understanding of APIs microservices event-driven systems serverless or containerized architectures distributed systems and enterprise integration patterns.
• Cloud and MLOps: Experience deploying AI services using cloud-native container or Kubernetes-based environments with CI/CD model serving observability monitoring and cost optimization.
• Responsible AI and security: Knowledge of data privacy secure AI patterns access controls content safety hallucination mitigation audit logging governance and healthcare compliance needs.
• Platform-agnostic tooling: Working knowledge of one or more AI platforms or frameworks such as Azure OpenAI Amazon Bedrock Google Vertex AI open-source LLMs LangChain LangGraph LlamaIndex Semantic Kernel MLflow or equivalent technologies.

Bachelors degree in Computer Science Computer Engineering Software Engineering Artificial Intelligence Data Science Biomedical Engineering or a related STEM discipline with 10 years of relevant software engineering experience. A masters degree or equivalent advanced experience in AI/ML distributed systems or healthcare technology is preferred.

• Experience delivering AI capabilities in regulated healthcare medical imaging clinical workflow DICOM or enterprise healthcare software environments.
• Experience with healthcare interoperability standards (FHIR HL7 DICOMweb) for grounding AI agents in clinical and operational data.
• Ability to translate ambiguous product or workflow needs into AI use cases technical designs evaluation plans and production deployment roadmaps.
• Strong understanding of Imaging360-style enterprise platforms that connect cloud services data pipelines integrations customer workflows and operational analytics.
• Hands-on experience with model experimentation prompt evaluation RAG quality measurement agent workflow testing and production observability dashboards.
• Familiarity with AgentOps/LLMOps observability including tracing agent steps tool-call telemetry drift detection and OpenTelemetry GenAI conventions.
• Experience with synthetic data generation and PHI-safe test data for AI evaluation in regulated environments.
• Experience with secure and compliant AI solution design including privacy-by-design PHI-sensitive workflows auditability data minimization and role-based access control.
• Ability to compare AI models frameworks and platforms objectively based on business fit performance cost safety governance scalability and maintainability.
• Demonstrated technical leadership in mentoring engineers reviewing designs establishing reusable patterns and influencing cross-functional teams without direct authority.
• Excellent communication skills with the ability to explain AI architecture limitations risks trade-offs and outcomes to engineering product quality regulatory and leadership stakeholders.