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enGen is seeking an AI Architect to shape the overall AI strategy and design scalable, secure systems that integrate with enterprise infrastructure. You will specify the high-level architecture for AI solutions, select technology stacks, and establish best practices for MLOps across engineering teams.
The role requires deep experience in distributed systems, cloud-native patterns, and governance for privacy and compliance, with cross-functional leadership across Data Science, DevOps, and Product
enGen
JOB SUMMARY
We are seeking a visionaryAI Architectto serve as the technical cornerstone of ourartificial intelligence strategy. While an AI Engineer builds the models, you will design the entire ecosystem they live in. You will be responsible for defining thehigh-level structure of our AI systems, ensuring they arescalable, secure, and seamlessly integrated with our enterprise infrastructure. As an Architect, you sit at the intersection of business value and technical feasibility, translating executive goals into robust technical blueprints.
This role involves defining theend-to-end architecture for AI solutions, encompassingdata pipelines, model storage, inference engines, and front-end integration. The individual will be responsible for evaluating and selecting the optimal "tech stack," including specificLLMs, vector databases, and orchestration frameworks, to meet the company's long-term needs. Key responsibilities also include establishingbest practices for MLOpsto ensure consistent coding standards, model versioning, and deployment strategies across the engineering team. The architect will designscalable systemscapable of handlingmassive data throughput and low-latency inferencefor real-time applications. A critical aspect of the role is architecting "Human-in-the-loop" systems and guardrails to ensuredata privacy, compliance (GDPR/CCPA), and AI safety. This position requires strongcross-functional leadership, acting as the technical liaison between Data Science, DevOps, and Product teams to align the AI roadmap with infrastructure capabilities.
Candidates should possessarchitectural expertise, including a deep understanding ofdistributed systems, microservices architecture, API design, and cloud-native patterns. Proficiency in designing complex data schemas for both structured and unstructured data, as well as experience withInfrastructure as Code (IaC)tools like Terraform or Ansible to manage AI infrastructure, is required. Technical proficiencies include expert-level knowledge ofcloud platforms such as AWS (SageMaker), Azure (Azure ML), or GCP (Vertex AI). Mastery of theAI lifecycle, from frameworks like PyTorch/TensorFlow to deployment tools like Kubeflow, MLflow, or BentoML, is essential. An expert understanding ofGenerative AI, including Transformer architectures,RAG (Retrieval-Augmented Generation), and fine-tuning methodologies, is also a key qualification.
The ideal candidate will have a proven track record of5+ years in software engineering or data science, with at least3 years in an architectural role. Experience inenterprise integration, particularly in migrating legacy systems toAI-augmented workflows, is highly valued. This role also demandsthought leadership, including the ability to mentor senior engineers and present complex architectural decisions to the C-suite.
Essential competencies for this role includestrategic thinking, with the ability to anticipate 12–24 months ahead in AI trends and prepare the infrastructure accordingly.Cost management skills, specifically optimizing cloud spend for GPU/TPU resources, are crucial.Exceptional communication skillsare also required, demonstrated through the ability to document architectural patterns and lead technical design reviews.
ESSENTIAL RESPONSIBILITIES
EDUCATION
Required
Substitutions