AI Architect

Project description

We are seeking a seasoned AI Architect with a strong background in enterprise‑scale AI, Machine Learning, and Generative AI systems. This role is ideal for someone who combines deep technical expertise with strategic vision—someone who can define the AI roadmap, architect complex LLM‑driven solutions, and guide engineering teams in building secure, scalable, and production‑ready AI platforms. You should bring at least 7+ years of experience in IT, including hands‑on work with modern AI/ML technologies, cloud platforms, and enterprise architecture patterns.

• Define and lead the enterprise AI & Generative AI strategy, including long‑term architecture roadmaps.
• Design end‑to‑end AI/ML and GenAI systems, covering data ingestion, feature engineering, model training, fine‑tuning, deployment, and monitoring.
• Architect LLM‑based solutions, including Retrieval‑Augmented Generation (RAG), prompt engineering frameworks, vector databases, and multimodal pipelines.
• Evaluate, fine‑tune, and deploy foundation models and LLMs for diverse business use cases.
• Build secure, scalable, high‑performance AI infrastructure on AWS, Azure, or GCP.
• Establish MLOps and LLMOps practices: CI/CD for models, model versioning, evaluation pipelines, observability, and automated testing.
• Implement Responsible AI, governance, data privacy, and security best practices across all AI and GenAI systems.
• Integrate AI/GenAI capabilities into enterprise applications, APIs, and microservices.
• Define evaluation frameworks for LLM performance, including hallucination detection, grounding, bias mitigation, and quality metrics.
• Optimize AI workloads for scalability, latency, and cost efficiency.
• Mentor AI/ML engineers, lead architecture reviews, and drive technical standards across teams.
• Stay current with advancements in LLMs, multimodal AI, vector search, and emerging generative technologies.

• 7+ years of experience in IT, including at least 3-5 years in AI/ML architecture or senior technical leadership roles.
• Proven experience designing and deploying AI/ML and Generative AI systems in production.
• Strong understanding of LLMs, RAG architectures, vector databases, and prompt engineering.
• Hands‑on experience with cloud platforms (AWS, Azure, or GCP) and cloud‑native AI services.
• Expertise in MLOps/LLMOps tools and practices (CI/CD, model registries, monitoring, evaluation).
• Solid knowledge of Python, ML frameworks (PyTorch, Tensor Flow), and modern data engineering tools.
• Experience with Responsible AI, governance frameworks, and enterprise‑grade security.
• Ability to translate business needs into scalable AI architectures and technical roadmaps.
• Strong communication skills and the ability to guide engineering teams and stakeholders.

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