Principal Software Engineering

Designs, develops and tests software systems or applications for software enhancements and new products including cloud-based or internet-related tools. Designs and develops security features of products including systems, applications and/or solutions. Ensures the security of all products is maintained throughout the product lifecycle. Integrates new security features, updates into existing products, and resolves integration and testing issues.

The AI Solution Architect will oversee the architecture for AI solution teams, focusing on modifying existing products and creating new ones. The ideal candidate should be passionate about designing, building, implementing, and maintaining AI/ML/Generative AI applications. Leadership skills are essential to implement the latest AI techniques and architectures and to continuously improve the AI/ML development, delivery, and operations process. The role involves adhering to best practices from Software Engineering, DevOps, MLOps, and LLMOps.

The AI Solution Architect will also be responsible for translating project requirements into strategic architecture solutions, ensuring the integration of cloud-native tools from major hyperscalers and machine learning to create chatbots, optimizations, and cognitive services. This role requires a blend of technical expertise and the ability to bridge the gap between intricate business challenges and transformative AI solutions, making it a strategically crucial position.

Define and oversee the AI/ML/GenAI technical direction and architectural vision, ensuring alignment with strategic goals and digital transformation efforts.
Data Analysis, Tool and Framework selection, Model Development, Testing and Deployment.
Key contributor in architecting a comprehensive AI Engineering framework that supports the deployment, evaluation, and management of ML models & GenAI solutions.
Select appropriate technologies from a pool of open-source and commercial offerings, considering deployment models and integration with existing tools.
Understand and contribute to MLOps and LLMOps focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models.
Collaborate with Enterprise, Application, Data & DevOps Architects, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot use cases and discuss architectural design.
Develop and maintain contact with top decision makers, lead proposal development, and contribute to pricing strategies.
Audit AI tools and practices across data, models and software engineering focusing on continuous improvement and feedback mechanisms.
Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
Own all communication and collaboration channels pertaining to assigned projects, including regular stakeholder review meetings and cross team alignments.
Work closely with the business, segment analytics, IT teams and partners to deliver the outcome and help drive adoption.
Hands-on prototyping of new technology solutions by working with cross teams

15+ Years of Experience operating on AWS Cloud or other cloud hyperscalers with building Data and AI Solutions
15+ Years of Experience Data Warehouses, Data Lakes and Data Modelling techniques
15 + years of experience in Data Science, Statistics, and Machine Learning
15+ years of experience in machine learning model development, natural language processing, and data analysis; Experienced in Supervised and Unsupervised learning, feature engineering, model training, and deployment.
15+ years of experience in implementing cloud-based AI/ML workloads on any cloud hyperscalers (AWS, Google or Azure).
Good understanding and implementation experience on GenAI models available with Cloud hyperscalers. AWS Bedrock experience is a plus but not required.
15+ Years of coding experience with Python, R, SQL etc.
Hands on experience working with LLM/RAG/Finetuning
Experience working on Agile projects and Agile methodology in general.
Experience in configuration management tools and defining configuration management schema
Excellent problem solving, communications, and teamwork skills.
Exceptional presentation, visualization, and analysis skills