AI Platform Architect
AceStack LLC
Job Title: AI Platform Architect
Location: Remote
Full time Job Description AI Platform Architect 3+ years hands-on experience designing and implementing AI/ML or Generative AI solutions in enterprise environments.
Strong experience with LLMs, prompt engineering, RAG, agent workflows, embeddings, vector databases and API-based AI services.
Strong cloud architecture knowledge across Azure, AWS or GCP, plus microservices, APIs, distributed systems and event-driven architecture.
Experience with Python, modern AI/ML frameworks, DevOps, MLOps/LLMOps, CI/CD, infrastructure automation, documentation and architecture governance.
Familiarity with AI governance, model risk management, responsible AI standards, security reviews, red teaming, guardrails and adversarial testing.
Experience integrating AI into ITSM, observability, knowledge systems, workflow engines and regulated enterprise environments.
Define end-to-end architecture for enterprise AI solutions including LLM-based applications, RAG, agentic workflows and model orchestration services.
Design secure, scalable, maintainable and cost-effective AI blueprints across cloud, hybrid and on-premises environments.
Architect prompt orchestration, contextual grounding, embeddings, vector database integration, retrieval quality, hallucination controls and traceability.
Embed security-by-design and responsible AI controls covering IAM, API protection, auditability, logging, monitoring, data leakage and prompt injection mitigation.
Define MLOps/LLMOps, CI/CD, deployment, observability, evaluation framework, production readiness checklist and reusable AI reference architectures.
Location: Remote
Full time Job Description AI Platform Architect 3+ years hands-on experience designing and implementing AI/ML or Generative AI solutions in enterprise environments.
Strong experience with LLMs, prompt engineering, RAG, agent workflows, embeddings, vector databases and API-based AI services.
Strong cloud architecture knowledge across Azure, AWS or GCP, plus microservices, APIs, distributed systems and event-driven architecture.
Experience with Python, modern AI/ML frameworks, DevOps, MLOps/LLMOps, CI/CD, infrastructure automation, documentation and architecture governance.
Familiarity with AI governance, model risk management, responsible AI standards, security reviews, red teaming, guardrails and adversarial testing.
Experience integrating AI into ITSM, observability, knowledge systems, workflow engines and regulated enterprise environments.
Define end-to-end architecture for enterprise AI solutions including LLM-based applications, RAG, agentic workflows and model orchestration services.
Design secure, scalable, maintainable and cost-effective AI blueprints across cloud, hybrid and on-premises environments.
Architect prompt orchestration, contextual grounding, embeddings, vector database integration, retrieval quality, hallucination controls and traceability.
Embed security-by-design and responsible AI controls covering IAM, API protection, auditability, logging, monitoring, data leakage and prompt injection mitigation.
Define MLOps/LLMOps, CI/CD, deployment, observability, evaluation framework, production readiness checklist and reusable AI reference architectures.
Vacancy posted 4 days ago
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