Design and develop architectural solutions utilizing foundational models, AI components, and scalable platforms to address complex business challenges.

Create detailed specifications for AI-based products and services, such as conversational agents, document intelligence systems, and personalized recommendation engines.

Build and implement frameworks like Retrieval-Augmented Generation (RAG) and Knowledge Graphs to enable complex data relationships and contextual understanding.

Lead technical planning, architecture development, and specification modifications to meet evolving business and technical needs.

Assess and ensure compatibility among AI platforms, tools, and models to deliver cohesive and high-performing solutions.

Collaborate with cross-functional teams, including product managers, data scientists, and engineers, to align AI solutions with business objectives and customer requirements.

Evaluate the effectiveness of deployed AI models, documenting insights and driving iterative improvements for enhanced performance and outcomes.

Stay updated on advancements in foundational models and emerging AI technologies, integrating state-of-the-art capabilities into solutions.

Develop and deploy scalable APIs and frameworks to integrate machine learning models into production environments for robust operational performance.

Mentor and train junior staff, providing technical guidance and sharing best practices to strengthen team capabilities.

Serve as a quality and best-practices resource, resolving complex challenges and offering expertise in specialized areas.

Ensure solutions are designed with scalability, security, and maintainability, supporting long-term organizational goals and growth.

At least 5 years of experience in a similar role/area

Educational Background: Master’s degree in Computer Science, Engineering, or a related field.

Programming Skills: Strong proficiency in Python and deep learning libraries (e.g., TensorFlow, PyTorch).

Proficiency in designing architectures leveraging foundational models, AI platforms, and deep learning technologies.

AI and NLP: Excellent understanding of natural language processing, including experience with language models (e.g., GPT, BERT, transformers) for text analysis and generation tasks.

Generative AI and RAG: Familiarity with Retrieval-Augmented Generation (RAG) methods and tools to enhance model output with relevant, context-based information retrieval.

Knowledge Graphs: Experience in building and querying Knowledge Graphs or similar data structures to support intelligent data connections is highly desirable.

Deep Learning: Strong knowledge of deep learning architectures and algorithms (e.g., CNNs, RNNs, attention models) and their application to real-world scenarios.

Data Processing and Analysis: Proficiency in data preprocessing, feature engineering, and analysis using tools like Pandas, NumPy, and SQL.

Cloud and Deployment: Experience with cloud platforms (AWS, GCP, or Azure) and deploying ML models in a production environment.

Big Data Tools: Basic understanding of big data frameworks (e.g., Spark, Hadoop) to manage and process large datasets.

Independence and Leadership. Operates independently to apply expertise in developing and deploying scalable AI components and platforms.