AI Architect

SOLVENTUM•Pittsburgh, PA

17h•$184,400 - $253,550•Remote

About The Position

Solventum is a new healthcare company with a long legacy of solving big challenges that improve lives and help healthcare professionals perform at their best. At Solventum, people are at the heart of every innovation we pursue. Guided by empathy, insight, and clinical intelligence, we collaborate with the best minds in healthcare to address our customers’ toughest challenges. As a AI Architect with deep expertise in Machine learning, Agentic AI, Generative AI, and Natural Language Understanding (NLU), you will lead high-impact AI Platform development for HIS applications. In this role, you will be a hands-on research and development leader - driving technical breakthroughs, designing novel AI architectures, and directly influencing the integration of advanced AI into mission-critical healthcare products. You will collaborate closely with other scientists, engineers, and domain experts to create platform/solutions that are explainable, reliable, and transformative for healthcare operations. This role is focused on building scalable AI platforms and frameworks from scratch and is not a data science or model experimentation role.

Requirements

Master’s in Computer Science, AI, Machine Learning, or related field AND 10+ years of experience in developing AI/ML platform on Cloud & on-Prem.
Or 15+ software engineering background in AI, Machine Learning, or related field with 10+ years of experience in building AI/ML platforms.
Proficiency in Python, and modern ML libraries.
Experience with GenAI, LLMs, transformer architectures, and advanced Model Routing (dynamically selecting and orchestrating open-source vs. proprietary models based on latency, cost, and capability tiers).
Skilled with AI development tools and frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex).
Deep expertise in building enterprise-grade RAG systems, including advanced chunking, hybrid search, vector database management, and retrieval optimization.
Hands-on experience with autonomous AI agents and reasoning systems, specifically mastering Agent-to-Agent (A2A) communication protocols and the Model Context Protocol (MCP) for multi-agent orchestration.
Advanced skills in Agent Context Management, including context window optimization, stateful memory injection, caching strategies, and managing long-running agent contexts.
Proven ability in Observability, Logging, and Scalable systems, including specialized LLM/Agent tracing to monitor reasoning steps, token usage, and system latency.
Strong track record applying AI architectures to scalable, generic enterprise platforms and complex use cases.
Strong background with cloud platforms, On-Prem deployments, and MLOps/LLMOps practices.

Nice To Haves

Experience with Knowledge Graph DBs (e.g., Neo4j), NoSQL/SQL databases, and native Vector databases, particularly in designing advanced GraphRAG and hybrid-retrieval architectures.
Strong background in containerization and infrastructure as code (Kubernetes, Terraform, etc.).
Familiarity with advanced multi-agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel) for complex, stateful workflow execution.
Proven ability to take complex AI Architecture/Design—especially non-deterministic LLM and multi-agent systems—from concept to highly available, production-grade deployments.
Experience with LLM Evaluation frameworks (e.g., Ragas, TruLens, LLM-as-a-Judge) to continuously monitor and score agent reasoning and RAG retrieval quality.
Knowledge of model fine-tuning techniques (LoRA, PEFT) and model distillation to create smaller, task-specific models that optimize cost and latency within the model routing layer.
Familiarity with implementing platform-wide AI guardrails, prompt injection defenses, and output validation mechanisms.

Responsibilities

Architect, design and develop AI platform to support large language models to handle healthcare-specific language, regulatory requirements, and ethical considerations.
Frameworks which can support AI pipelines that can process structured and unstructured healthcare data (FHIR, HL7, clinical notes, claims data).
Contribute to domain-specific model architectures that improve clinical decision-making, revenue cycle management, and patient engagement.
Serve as the primary technical authority on any AI Platform related development within product teams.
Mentor junior AI designers and engineers through code reviews, research guidance, and technical workshops.
Drive internal knowledge-sharing on emerging AI trends, frameworks, and best practices.
Implement rigorous model evaluation frameworks for accuracy, robustness, and fairness.
Ensure compliance with healthcare privacy and data security regulations (HIPAA, HITRUST).
Partner with engineering to move research prototypes into production environments.