AI Architect

Responsibilities: Interact with clients or internal partners to understand and refine AI requirements. Design and present tailored AI solutions to clients, aligning with their business goals and challenges. Architect scalable and maintainable AI systems that align with both short-term and long-term goals. Identify the best algorithms and models based on the problem type and data specifics. Choose appropriate cloud platforms, hardware, and software setups, ensuring optimal performance and cost-effectiveness. Plan data ingestion, preprocessing, and transformation pipelines, and ensure high-quality data sourcing and annotation. Strategize the integration of AI solutions with existing systems and workflows. Build prototypes or minimum viable products (MVP) to validate solutions and gather feedback. Oversee the development and training of AI models, ensuring standard methodologies are followed. Implement versioning for data, models, and code to ensure reproducibility and traceability using tools like DVC or MLflow. Set metrics and benchmarks to evaluate model performance and ensure models meet business objectives. Refine models and systems based on performance feedback, ensuring iterative improvement. Plan and execute strategies for deploying models into production, considering factors like scalability, latency, and monitoring needs. Ensure thorough documentation of architecture, model details, training processes, and deployment instructions for both technical and non-technical partners. Work closely with data scientists, engineers, and other team members, promoting a collaborative environment. Continuously monitor emerging trends and advances in AI to ensure the company's solutions remain innovative. Monitor deployed models for performance degradation, anomalies, or drifts. Offer guidance, training, and mentorship to junior team members or partners unfamiliar with AI concepts. Periodically retrain or fine-tune models with new data or in response to changing business environments. Gather feedback from end-users or clients to improve solutions and align better with user needs. Act promptly to resolve any issues related to deployed AI solutions.

Required Skills: At least one significant project experience architecting and deploying AI solutions on a cloud platform using Azure. Hands-on experience in managing end-to-end lifecycle for at least two models, including training, validation, deployment, monitoring, and maintenance. Lead the design of advanced AI architectures, ensuring readiness for production environments. Apply innovative techniques in deep learning and transfer learning to enhance model performance and adaptability. Regularly assess the efficiency and effectiveness of architectures, refining algorithms, data structures, and configurations as necessary. Architect NLP solutions on cloud platforms and integrate with various databases ensuring optimal performance. Stay updated on emerging technologies and methodologies in software engineering. Partner with multi-functional teams to align on objectives and deliverables. Deep Learning Frameworks: Proficient in TensorFlow, Keras, PYTorch. Advanced experience with Spark. Strong coding skills in Python and Pyspark, Knowledge of advanced Client Libraries including: scikit-learn, XGBoost, LightGBM etc. Proficiency with GPT. Containers & Orchestration: Familiarity with Docker, Kubernetes. Experience with Flask, Django for model deployment. Knowledge of SQL.