AI Architect
AI Architect
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Job Description
Job Title: AI Architect
Overview
A fast-growing technology team is looking for a hands-on AI Architect to lead the design development and deployment of scalable AI-powered solutions. This role demands deep technical expertise in artificial intelligence machine learning and cloud-native systems with a focus on building real-world applications through rapid prototyping and iterative development. You ll work closely with cross-functional teams to lead both internal and customer-facing projects taking AI solutions from concept to production.
Key Responsibilities
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Design scalable AI/ML architectures for enterprise applications using cloud-native tools and best practices.
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Lead full ML lifecycle: data ingestion feature engineering model training evaluation deployment and monitoring.
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Apply advanced ML techniques such as deep learning transformers reinforcement learning and generative models to solve business problems.
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Provide technical mentorship to ML engineers and data scientists including code reviews and model performance tuning.
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Implement MLOps practices including CI/CD for ML model versioning reproducibility and standardized pipelines.
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Champion responsible AI standards fairness interpretability privacy and compliance.
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Optimize AI workloads on major cloud platforms like AWS Azure or GCP.
Required Qualifications
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Bachelor s or Master s in Computer Science Data Science or AI (PhD preferred).
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8 years in AI/ML solution development with at least 3 years in an architectural or leadership role.
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Strong coding skills in Python and experience with ML frameworks like TensorFlow PyTorch scikit-learn Keras and SageMaker.
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Solid understanding of algorithms data structures probability statistics and optimization techniques.
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Practical experience using cloud-based AI/ML tools such as SageMaker Vertex AI and Azure ML.
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Track record of deploying ML models to production at scale.
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Experience working with data lakehouse environments distributed data processing frameworks like Spark and orchestration tools like Airflow.
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Hands-on experience with MLOps: deployment monitoring and retraining pipelines.
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Experience with OCR Generative AI and LLMs (e.g. ChatGPT Claude Gemini).
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Familiarity with generative models like GPT DALL E Stable Diffusion and prompt engineering.
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Exposure to edge AI large-scale NLP and computer vision.
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Experience with container technologies (Docker Kubernetes) for AI deployment.
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Proficiency with cloud services (AWS Azure GCP).
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Strong communication and leadership skills able to translate complex ML topics into actionable strategies.
Employment Type
Full Time
