AI Architect

Position: AI Architect ResponsibilitiesClient Engagement & Solution DesignCollaborate with clients and internal stakeholders to understand AI requirements. Design, present, and deliver AI solutions aligned with business goals and challenges. Develop scalable and maintainable AI architectures that balance short- and long-term needs. Architecture & System DesignIdentify the most suitable algorithms and models based on data and problem specifics. Select optimal cloud platforms, hardware, and software to ensure performance and cost-effectiveness. Plan and oversee data pipelines, including ingestion, preprocessing, transformation, and annotation. Integrate AI solutions seamlessly into existing systems and workflows. Prototyping & DevelopmentBuild prototypes or MVPs to validate solutions and gather feedback.

Supervise the development and training of AI models, applying industry best practices. Implement robust versioning for data, models, and code (e. g. , MLflow, DVC) to ensure reproducibility. Performance & OptimizationDefine benchmarks and KPIs to evaluate model performance and ensure alignment with business objectives. Refine models iteratively based on evaluation results and stakeholder feedback. Monitor deployed models for performance drift, anomalies, or degradation. Deployment & OperationsLead strategies for model deployment into production, considering scalability, latency, and monitoring. Ensure comprehensive documentation of architecture, models, training, and deployment processes. Periodically retrain and fine-tune models with new data or evolving business requirements.

Collaboration & LeadershipWork closely with data scientists, engineers, and cross-functional partners in a collaborative environment. Mentor junior team members and provide training on AI concepts and practices. Stay current with emerging trends in AI, ML, and cloud technologies to keep solutions innovative. Act promptly to resolve issues related to deployed AI systems and ensure business continuity.  Required Skills & QualificationsProven experience leading at least one end-to-end AI solution architecture and deployment on a cloud platform (preferably Azure). Hands-on experience managing full lifecycles of at least two AI/ML models, including training, validation, deployment, monitoring, and maintenance.

Strong track record in designing production-ready AI architectures and applying advanced techniques in deep learning and transfer learning. Experience architecting NLP solutions on cloud platforms and integrating with diverse databases. Proficiency in Python and PySpark with advanced coding skills. Expertise in deep learning frameworks: TensorFlow, Keras, PyTorch. Strong background with Spark for large-scale data processing. Experience with popular ML libraries: scikit-learn, XGBoost, LightGBM. Familiarity with Generative AI frameworks including GPT-based models. Skilled in containerization and orchestration using Docker and Kubernetes. Experience deploying models with Flask or Django. Proficient in SQL for data manipulation and integration.

Strong grasp of software engineering best practices for scalable AI development. Fluency in written and spoken English and Spanish is required.