AI Architect

5 days ago

Kochi, India DMAIC Academy & Consultancy Full time

AI Architect Role SummaryThe AI Architect is responsible for designing the end-to-end architecture, frameworks that enable scalable, and high-performance AI systems both within the organization and for product teams. This role bridges machine learning, software engineering, and cloud infrastructure to create a cohesive enterprise AI ecosystem. The AI Architect defines reference architectures, accelerates solution teams, ensures compliance, and sets the technical direction for how AI is built, deployed, and governed.Key ResponsibilitiesDesign the AI architecture for the enterprise including inference layers, vector stores, data ingestion, orchestration, and monitoring.Architect scalable LLM/RAG systems, agent frameworks, and generative AI services that can be reused across domains and business units.Define standards for embeddings, vectorization, prompt orchestration, caching layers, and evaluation pipelines.Establish patterns for developing, fine-tuning, and deploying ML/LLM models Evaluate when to use foundation models, when to fine-tune, and when to build custom models.Define and enforce AI architecture principles, security policies, and compliance (HIPAA, FDA, ISO).Implement guardrails for privacy, PHI/PII protection, safe model usage, hallucination risk mitigation, audit logging, and explainability.Partner with data engineering, IT security, cloud infrastructure, and product teams to ensure architectural alignment.Participate in roadmap planning and technology selection for the AI/ML ecosystem.Conduct build-vs-buy assessments for AI platforms, tokenization, data protection, vector databases, model hosting, and MLOps tools.Required QualificationsBachelor’s or Master’s in Computer Science, Engineering, AI/ML, or related field; equivalent experience considered.5+ years of experience in ML/AI engineering, data engineering, platform engineering, or cloud architecture.Strong proficiency in distributed systems, cloud architecture (Azure), and containerization (Kubernetes).Hands-on experience designing and deploying ML/LLM systems in production.Expertise with ML frameworks (PyTorch, TensorFlow), MLOps tools (MLflow, KServe, Kubeflow, Airflow), and vector databases.Deep understanding of LLM/RAG patterns, embeddings, prompt engineering, caching layers, and model evaluation.Preferred QualificationsExperience with agent frameworks (LangChain, OpenAI Agents API).Experience in highly regulated industries (healthcare, MedTech, pharma).Experience with encryption, tokenization, PHI/PII protection, or secure ML workflows.

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