AI Architect

GENZEON SERVICES

GenAI, Agentic AI & AI-Native Engineering

EXPERIENCE

8 - 12 years

LEVEL

Senior Architect

LOCATION

India (Hybrid) - Genzeon offices

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

• •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.