Professional Software/Data Scientist/Engineer

TinyURL

Professional Software/Data Scientist/Engineer focusing on AI solutions for digital and data-intensive systems at Geosyntec. Involves designing enterprise-grade architectures and collaborating across functions.

Posted 5/8/2026full-timeKennesaw • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AzureCloudJavaPython

About the role

Key responsibilities & impact

Design and lead AI-based/digital solutions and services, selecting optimal architectures aligned with the company’s cloud, data, and security standards.
Translate business requirements into implementable architectures and solutions across data platforms, AI models, application services, orchestration frameworks, and extensibility layers.
Design, develop, test, deploy, support and enhance new or off-the-shelf secure AI and ML models, including Retrieval-Augmented Generation (RAG), tailored to domain-specific deliverables and company initiatives.
Build the Generative AI platforms, systems and infrastructure and apply general knowledge of software development and infrastructure as code practices.
Automate engineering and business processes using scripting languages and modern automation platforms, including Python and cloud-based AI services from Microsoft (e.g., Copilot Studio, Azure Foundry, etc.).
Design and implement user interfaces, data visualizations, and other interaction layers as needed to support AI-enabled workflows and decision-making.
Develop Python scripts for data analysis and automation of engineering analysis.
Integrate front-end components with back-end APIs and services for seamless data interactions.
Ensure UI/UX best practices to improve user experience and workflow efficiency.
Lead solution architecture for priority AI, generative AI, and agent-based use cases, including hands-on technical design, decision-making, and architecture reviews.
Identify technical, security, data, and delivery risks associated with AI solutions and implement appropriate mitigation strategies.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Information Systems, Data Science or related field. (required)
At least 5 years (7+ preferred) of related work experience or equivalent combination of education and experience. (required)
Demonstrated experience in software engineering, working with large data sets, and developing end-to-end workflows in a professional environment. (required)
Proficiency in at least one modern programming language (for example, Python, C#, Java, or similar) and ability to write maintainable, testable code. (required)
Proven experience in developing and deploying AI/ML models, with a strong understanding of RAG models and their implementation using LLMs. (required)
Proficiency with vector stores, semantic search, and related technologies. (required)
Knowledge of fine-tuning techniques for LLMs to adapt them to specific tasks and domains is required. (required)
Familiarity with other AI and ML technologies such as reinforcement learning, computer vision, or recommendation systems. (preferred)
Experience with or ability to learn and work with Microsoft Azure services, Microsoft Foundry, Azure OpenAI, Copilot Studio, Power Platform, cognitive search, vector databases, and API integrations. (required)
Strong understanding of identity, networking, data governance, and security within cloud-based environments. (required)
Demonstrated ability to lead technical design discussions, manage stakeholders, and discuss with engineering teams. (required)
Strong communication skills and ability to translate needs between business stakeholders and technical teams. (required)
Valid U.S. driver’s license and a satisfactory driving record for business travel. (required)

Benefits

Comp & perks

Competitive pay and benefits
Well-being programs to support you and your family

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI modelsML modelsPythonC#JavaRAG modelsfine-tuning techniquesdata analysisautomationsoftware engineering

Soft Skills

leadershipstakeholder managementcommunicationtechnical design discussionsworkflow efficiency