The Senior AI Architect will be responsible for defining the technological vision and leading the implementation of next‑generation Artificial Intelligence architectures. This role combines deep technical expertise, strategic thinking, and strong business alignment, ensuring that AI/GenAI solutions are scalable, secure, and deliver real impact.
Key Responsibilities
Architecture & Strategy
- Design the target architecture for AI ecosystems, including Copilots/Agents, Azure OpenAI, AI Search, data platforms, integrations, and pipelines, ensuring end‑to‑end alignment with customer objectives.
- Define technical standards, guardrails, security practices, domain blueprints, and architectural guidelines, ensuring cross‑organizational consistency.
- Translate business needs into structured technical solutions, leading workshops, discovery sessions, and technical assessments.
- Assess trade‑offs between performance, cost, risk, and complexity when defining AI/GenAI solutions.
Technical Design & Implementation
- Lead the design of scalable, secure, high‑performance AI/GenAI architectures.
- Select the appropriate technologies, frameworks, and models (LLMs, RAG, vector DBs, multi‑agent systems, MLOps, automation tooling).
- Build and validate PoCs and reference architectures to accelerate AI adoption.
- Integrate models, agents, and data pipelines with APIs, microservices, core business applications, and enterprise data sources.
Governance, Quality & Security
- Establish principles for technical security, data isolation, compliance, observability, and model governance.
- Ensure adherence to best practices in performance, resilience, scalability, and privacy.
- Define mechanisms for control, auditability, and continuous improvement of AI architectures in production.
- Work closely with IT and Security teams to ensure consistent integration with the customer’s enterprise architecture.
- Ensure architectural consistency across multiple AI use cases and domains, particularly in large‑scale AI programs.
Technical Leadership & Collaboration
- Act as a technical authority for internal and external stakeholders, including Data, Engineering, Product teams, and C‑level leadership.
- Mentor Data Scientists, ML Engineers, and Software Engineers, promoting technical excellence and best practices.
- Support pre‑sales, RFPs, solution proposals, and technology vision for strategic clients.
- 6–10 years of experience in software development, solution architecture, data engineering, or AI.
- Solid experience in AI/ML and GenAI, including LLMs, pipelines, RAG, and enterprise integration.
- Strong knowledge of ML frameworks such as TensorFlow, PyTorch, Scikit‑learn.
- Significant experience with Azure (preferred), AWS or GCP, CI/CD, Docker/Kubernetes, and microservices.
- Strong understanding of AI‑related security, cloud architectures, and MLOps practices.
- Excellent communication skills, both technical and non‑technical.
- Proven experience in technical leadership and architectural standards definition.
- Experience in technology consulting and client interaction.
- Certifications such as Azure AI Engineer / Solution Architect, AWS ML Specialist, Google Cloud ML.
- Knowledge of architecture frameworks: TOGAF, DDD, Clean Architecture, SAFe.
- Participation in conferences, talks, or thought leadership initiatives.