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Job details:
AI Architect
Experience : 8 - 12 years
Employment Type : Full-Time
Function : Technology / AI & Engineering
Reports To : Head of AI / Engineering Leadership
About Genzeon
Genzeon Services is a technology and business process solutions company that helps healthcare payers, providers, and life sciences organizations modernize their operations through digital transformation, automation, and applied AI. We combine deep domain expertise with modern engineering practices to build intelligent, scalable systems that solve real business problems.
Role Summary
We are looking for an experienced AI Architect who has worked extensively on AI-led projects and AI-Native Engineering to design, build, and scale intelligent systems across our client engagements. This role goes beyond bolting AI onto existing applications - the ideal candidate has architected solutions where AI (including LLMs and agentic systems) is a first-class, foundational component of the system design. You will define architecture standards, lead technical delivery, and act as the primary AI technology advisor across multiple engagements and teams.
Key Responsibilities
AI-Native Solution & Enterprise Architecture :
- Architect end-to-end AI-native systems - applications designed from the ground up around AI capabilities rather than retrofitted with AI features.
- Define reference architectures, design patterns, and technical standards for AI/ML and GenAI solutions across the organization.
- Evaluate and select the right architecture (RAG, fine-tuning, agentic workflows, hybrid retrieval, multi-model orchestration) based on business requirements, cost, latency, and accuracy trade-offs.
- Own technical decision-making on model selection, data architecture, integration patterns, and scalability for AI systems.
- Ensure AI solution designs address security, data privacy, compliance (including healthcare/HIPAA where applicable), and responsible AI principles.
GenAI, LLMs & Agentic AI
- Design and oversee implementation of LLM-powered applications, including RAG pipelines, prompt engineering frameworks, and fine-tuning/adaptation strategies.
- Architect multi-agent and agentic AI systems (planning, tool use, memory, orchestration) for complex, multi-step business workflows.
- Drive adoption of frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent for building production-grade agentic solutions.
- Stay current with the rapidly evolving GenAI ecosystem (foundation models, vector databases, evaluation frameworks) and translate emerging capabilities into practical solution designs.
MLOps & AI Platform Engineering
- Define and guide implementation of MLOps/LLMOps practices - CI/CD for models and prompts, automated evaluation, monitoring, and observability for AI systems in production.
- Architect scalable AI infrastructure across cloud platforms (Azure, AWS, or GCP), including vector stores, model-serving layers, and orchestration pipelines.
- Establish practices for model/prompt versioning, cost monitoring, performance benchmarking, and continuous improvement of deployed AI systems.
- Partner with data engineering to ensure high-quality, well-governed data pipelines feeding AI systems.
Leadership & Stakeholder Collaboration
- Act as the primary technical advisor on AI architecture for client engagements, presales, and internal capability building.
- Collaborate with product owners, business stakeholders, and delivery teams to translate business problems into feasible, well-architected AI solutions.
- Mentor engineers and data scientists on AI-native design principles, MLOps practices, and responsible AI development.
- Contribute to proposals, solution estimations, and technical due diligence for new AI opportunities.
Required Qualifications
- 8 - 12 years of overall technology experience, with at least 4 - 5 years focused substantially on AI/ML architecture and delivery.
- Demonstrated track record architecting and delivering production AI systems - not just prototypes or POCs.
- Hands-on depth in Generative AI and LLMs: RAG architectures, prompt engineering, embeddings, vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus), and fine-tuning approaches.
- Practical experience designing or implementing agentic AI systems (multi-agent orchestration, tool-calling, autonomous workflows).
- Strong foundation in traditional ML/AI (supervised/unsupervised learning, model evaluation) in addition to GenAI.
- Proficiency in Python and familiarity with ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, scikit-learn).
- Solid understanding of cloud-native architecture and AI services on Azure, AWS, or GCP (e.g., Azure OpenAI, AWS Bedrock, Vertex AI).
- Experience with MLOps/LLMOps tooling and practices - containerization (Docker/Kubernetes), CI/CD, model monitoring, and observability.
- Strong software architecture fundamentals - APIs, microservices, event-driven design, and system integration patterns.
- Excellent communication skills with the ability to explain complex AI concepts to both technical and non-technical stakeholders.
Preferred / Good To Have
- Experience in healthcare, payer/provider, or life sciences domains.
- Familiarity with agentic frameworks such as LangGraph, AutoGen, CrewAI, or Semantic Kernel.
- Exposure to responsible AI practices - model governance, bias evaluation, and explainability.
- Prior experience in a client-facing architect or technical lead role within a consulting or IT services environment.
- Relevant certifications (e.g., Azure AI Engineer, AWS Machine Learning Specialty, Google Cloud ML Engineer).
Education
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).
What We Offer
The opportunity to architect cutting-edge AI-native systems at enterprise scale, work alongside a collaborative engineering culture, and shape Genzeon's AI capability and delivery standards across high-impact client engagements.
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