Role Summary
We are seeking a highly experienced AI Solutions Architect to design, architect, and lead the implementation of enterprise-scale AI, Generative AI, LLM, and Agentic AI solutions. The ideal candidate will have strong experience translating complex business requirements into scalable AI architectures and hands-on expertise across cloud platforms, machine learning, LLMs, RAG, AI agents, APIs, microservices, and data platforms.
The architect will work closely with business stakeholders, data scientists, software engineers, cloud teams, security teams, and product leaders to define AI strategies and deliver secure, scalable, production-ready solutions.
Key Responsibilities
- Design enterprise AI and Generative AI architectures aligned with business and technology objectives.
- Lead architecture for LLM, RAG, Agentic AI, and multi-agent applications.
- Define end-to-end AI solution architecture covering data ingestion, processing, model orchestration, inference, APIs, applications, monitoring, and security.
- Evaluate and select appropriate LLMs, foundation models, embedding models, vector databases, AI frameworks, and cloud AI services.
- Design and implement RAG pipelines using document processing, chunking, embeddings, retrieval, reranking, and context orchestration.
- Architect AI agents and multi-agent workflows with tool calling, planning, memory, orchestration, and guardrails.
- Develop scalable AI solutions using Python, REST APIs, microservices, and event-driven architectures.
- Integrate AI capabilities into existing enterprise applications and business workflows.
- Design cloud-native AI platforms using AWS, Azure, and/or Google Cloud Platform.
- Establish architecture standards for LLMOps/MLOps, CI/CD, model deployment, observability, and lifecycle management.
- Collaborate with data engineering teams on data lakes, lakehouses, ETL/ELT pipelines, vector stores, and knowledge platforms.
- Design AI systems with enterprise requirements for security, privacy, governance, compliance, and responsible AI.
- Implement controls against prompt injection, data leakage, model abuse, hallucination, unauthorized tool access, and adversarial inputs.
- Define AI evaluation strategies covering accuracy, relevance, hallucination, latency, cost, safety, and model performance.
- Conduct architecture reviews, proof-of-concepts, technology evaluations, and technical feasibility assessments.
- Provide technical leadership and mentorship to AI/ML engineers and development teams.
- Communicate complex AI architecture concepts to both technical and non-technical stakeholders.
Required Technical Skills
AI / Generative AI
- 10+ years of overall software/technology experience with significant AI/ML architecture experience.
- Strong hands-on experience with Generative AI, LLMs, RAG, and Agentic AI.
- Experience with OpenAI, Azure OpenAI, Amazon Bedrock, Google Vertex AI, or similar foundation-model platforms.
- Strong understanding of:
- LLM architecture
- Prompt engineering
- Embeddings
- Tokenization
- Context windows
- Fine-tuning
- RAG
- Function/tool calling
- AI agents
- Model evaluation
- Guardrails
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