Description
We are seeking an AI Architect, a senior technical leader responsible for designing and overseeing end-to-end AI systems that align with business objectives.
Responsibilities:
- Design end-to-end AI systems (data ingestion → model deployment → UI).
- Collaborate with AI/ML engineers, Data engineers, and MLOps for production readiness.
- Strategic Planning & Business Alignment: Define AI roadmap aligned with organizational goals, Identify high-impact AI use cases and assess ROI, Engage stakeholders early for feasibility and governance.
- Architecture & System Design: Design AI system architecture (data pipelines, model lifecycle, orchestration), Integrate AI with enterprise apps and internal systems, Ensure scalability, fault tolerance, and cost efficiency.
- Model & Data Management: Select appropriate ML/DL models and frameworks (TensorFlow, PyTorch), Define versioning, fine-tuning, and deployment strategies, Implement CI/CD for ML pipelines and monitoring.
- Security, Compliance & Governance: Enforce data privacy, ethical AI practices, and audit trails, Implement guardrails for LLMs and prompt safety, Collaborate with security teams for risk mitigation.
- Cross-Functional Leadership: Mentor engineers and data scientists, Translate business requirements into technical tasks, Drive collaboration across product, data, and operations teams.
Education and Experience Required:
- Bachelor's or master’s degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline.
- Typically, 10+ years’ experience.
Knowledge and Skills:
- Programming & Software Engineering: Python, Java, Node.js; API design; microservices.
- Cloud & Distributed Systems: AWS, Azure, GCP; container orchestration.
- Technical Expertise: Strong knowledge of AI/ML algorithms, LLMs, RAG, and agent-based systems.
- Proficiency in Python and API integration.
- AI/ML Expertise: Model selection, NLP frameworks like TensorFlow, PyTorch, Hugging Face.
- Experience with MLOps, containerization (Docker/Kubernetes), and cloud platforms (AWS/Azure/GCP).
- System design for distributed AI applications.
- Data architecture (ETL, feature stores, vector databases).
- Security and compliance for AI systems.
- Strong communication skills are necessary to collaborate with cross-functional teams effectively, explain complex AI concepts to non-technical stakeholders, and provide technical leadership and mentorship to junior engineers.