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.
- Robust 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

📌 AI Native Architect (Hyderabad)
🏢 GENZEON (INDIA) PRIVATE
📍 Hyderabad