AI Architect
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Key skills for this role
About the Role
We are seeking an experienced AI Architect to define how we design, secure, and scale AI across the organization. You ll translate business strategy into reference architectures.
Key Skills for This Role
Responsibilities
- Define AI strategy and reference architectures for generative AI, agentic systems, RAG, and predictive ML
- Set model strategy: build vs buy, open weight vs. hosted, multi model routing, and evaluation framework
- Architect the data and retrieval foundation: lakehouse, feature stores, vector stores, and lineage
- Design the LLMOps/MLOps platform: experiment tracking, model and prompt registries, automated eval gates, and observability
- Lead AI security: threat modeling for prompt injection and data exfiltration, and OWASP LLM Top 10 mitigations
- Own responsible AI and governance: risk classification, human oversight, auditability, and alignment with regulations
- Establish FinOps for AI: cost models, token budgeting, and unit economics per use case
- Provide technical leadership, review designs, mentor engineers, and align stakeholders
Requirements
- 8+ years in software/data engineering, with 3+ years architecting AI/ML or large scale data systems
- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or related field
- Demonstrated experience delivering AI solutions in an enterprise or client facing/consulting environment
- Deep understanding of LLM and ML system design: RAG, agents, fine tuning, evaluation, serving, and full MLOps/LLMOps lifecycle
- Strong cloud architecture skills (Azure, AWS, or GCP) plus modern data platform (Databricks or Snowflake)
- Expertise in AI security, responsible AI, and data governance, with awareness of relevant regulations
- Excellent communication able to influence both engineers and executives
- Hands on enough to prototype, review code, and earn engineering teams' trust
Full Job Posting
Role Overview
- We are seeking an experienced AI Architect to define how we design, secure, and scale AI across the organization.
- You'll translate business strategy into reference architectures, platform decisions, and guardrails that engineering teams execute against.
- Increasingly, that means architecting agentic systems, multi model routing, and responsible AI controls that comply with regulations like the EU AI Act.
Key Responsibilities
- Define AI strategy and reference architectures for generative AI, agentic systems, RAG, and predictive ML.
- Set model strategy: build vs buy, open weight vs. hosted, multi model routing, and a framework for evaluating new models.
- Architect the data and retrieval foundation: lakehouse, feature stores, vector stores, and lineage that AI systems depend on.
- Design the LLMOps/MLOps platform: experiment tracking, model and prompt registries, automated eval gates, and observability.
- Lead AI security: threat modeling for prompt injection and data exfiltration, and OWASP LLM Top 10 mitigations.
- Own responsible AI and governance: risk classification, human oversight, auditability, and alignment with EU AI Act, NIST AI RMF, and ISO/IEC 42001.
- Establish FinOps for AI: cost models, token budgeting, and unit economics per use case.
- Provide technical leadership, review designs, mentor engineers, and align stakeholders from engineering to the executive team.
Candidate Requirements
- Demonstrated experience delivering AI solutions in an enterprise or client facing/consulting environment.
- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field.
- 8+ years in software/data engineering, with 3+ years architecting AI/ML or large scale data systems.
- A track record of designing and delivering production AI systems end to end, ideally including GenAI/LLM workloads.
- Deep understanding of LLM and ML system design: RAG, agents, fine tuning trade offs, evaluation, serving, and the full MLOps/LLMOps lifecycle.
- Strong cloud architecture skills (Azure, AWS, or GCP) plus a modern data platform (Databricks or Snowflake).
- Expertise in AI security, responsible AI, and data governance, with awareness of relevant regulations.
- Excellent communication able to influence both engineers and executives.
- Hands on enough to prototype, review code, and earn engineering teams' trust.
Nice to Have
- Architecting AI for regulated or sovereign environments (finance, healthcare, government, GCC/UAE).
- Enterprise architecture frameworks (TOGAF) and well architected reviews.
- Defining golden path tooling for internal AI development.
- Running structured vendor PoCs and bake offs.
Tools & Technologies You'll Work With
- Patterns: RAG, agentic/multi agent, event driven, lakehouse/medallion; reusable blueprints.
- Model strategy: multi model routing and gateways (LiteLLM, Portkey); Claude, GPT, Gemini, Llama, Mistral.
- Platforms: Azure AI Foundry, AWS Bedrock, Vertex AI, Databricks Mosaic AI.
- Data foundation: Delta Lake/Databricks, Snowflake, Unity Catalog, vector stores, knowledge graphs.
- LLMOps/MLOps: MLflow, LangSmith/Langfuse, model and prompt registries, eval in CI.
- Security & governance: OWASP LLM Top 10, NIST AI RMF, ISO/IEC 42001, EU AI Act, RBAC/ABAC, secrets management.
- Infra & observability: Kubernetes, Terraform/OpenTofu, OpenTelemetry; token/cost dashboards.
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