Job Description

Role Overview
The Analytics and Innovation Engineer is responsible for developing high-value analytics solutions and applications that address evolving business needs. This role plays a key part in driving AI initiatives, with a focus on designing and delivering internal LLM-powered tools and applications.

Working in a collaborative environment, you will translate business requirements into Minimum Viable Products (MVPs) that demonstrate incremental value. You will also help establish foundational engineering practices, including source control, code management, and CI/CD processes, while contributing to the delivery of scalable, production-ready solutions.

Key Responsibilities:

Application Development & Architecture

• Architect, prototype, test, and deliver cloud-based applications that provide rapid business value.
• Design, develop, and deploy LLM-powered applications, transitioning proof-of-concepts into scalable production solutions.
• Collaborate with technical teams and cloud infrastructure specialists to move prototypes into sustainable production environments.
• Participate in Agile ceremonies including sprint planning, backlog grooming, stakeholder demonstrations, and daily standups to ensure timely delivery.
• Contribute to establishing software engineering practices, including source control, code management, and CI/CD pipelines.
• Participate in code reviews, define development standards, and promote engineering best practices to maintain high-quality solutions.
• Provide recommendations on technology stack design, architecture, and process improvements.

Qualifications:

Education

• Bachelor's degree in Computer Science, Computer Engineering, Mathematics, Business, or a related field

Experience

• 5+ years of experience in software engineering, analytics, or related technology roles, with a proven record of delivering successful applications.
• Strong background in full-stack development, advanced analytics, data science, and/or application development.

Technical Skills:

AI and Machine Learning

• Hands-on experience with Large Language Models (LLMs) and implementing generative AI solutions in production environments.
• Experience designing, developing, and supporting AI-driven applications.

Data Engineering

• Strong understanding of data engineering and data lifecycle management.
• Experience preparing structured and unstructured datasets for AI and analytics applications.
• Knowledge of model evaluation techniques and performance benchmarking.

Languages and Frameworks

• Advanced proficiency in technologies such as:
• Python
• Scala
• Apache Spark
• Angular
• React
• Other modern application development frameworks

DevOps and Engineering Practices

• Experience with source control and CI/CD processes.
• Familiarity with tools such as:
• Git
• Bitbucket
• Jenkins
• Docker
• Other DevOps and automation platforms

Preferred Competencies

• Experience building scalable cloud-based applications and analytics platforms.
• Strong problem-solving and communication skills.
• Ability to work in Agile environments and collaborate effectively with cross-functional teams.
• Passion for emerging technologies and continuous improvement.