Senior AI Engineer

******Job Description Summary****** =================================== 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. **Job Description** =================== Roles and Responsibilities ========================== 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. Technical Skill Set =================== * **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. Education Qualification ======================= Bachelor’s 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 master’s degree or equivalent advanced experience in AI/ML, distributed systems, or healthcare technology is preferred. Desired Characteristics ======================= * 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. ******Additional Information****** ================================== **Relocation Assistance Provided:** Yes