The AI Architect is responsible for defining, designing, and governing end‑to‑end AI and GenAI solutions that are scalable, secure, and aligned with business strategy. This role bridges business needs, data engineering, ML engineering, and cloud platforms to deliver production‑grade AI systems across the enterprise.
Technical Skills
- Strong experience in AI architecture, machine learning, and deep learning concepts.
- Hands‑on knowledge of Python and ML frameworks (TensorFlow, PyTorch, Scikit‑learn).
- Experience with GenAI and LLM ecosystems (OpenAI, Azure OpenAI, Anthropic, open‑source models).
- Expertise in MLOps tools and practices (CI/CD, model registries, monitoring).
- Experience with data platforms (Data Lakes, Snowflake, BigQuery, Databricks).
- Knowledge of API‑based AI integration, MCP, microservices, and event‑driven architectures.
Cloud & DevOps
- Strong experience with at least one major cloud platform: Azure, AWS, or GCP.
- Familiarity with containerization and orchestration (Docker, Kubernetes).
- Understanding of infrastructure‑as‑code and DevSecOps practices.
Experience
- 8–12+ years of overall IT experience, with 3–5+ years in AI/ML solution or platform architecture roles.
- Proven experience delivering AI solutions into production at scale.
