Lead Software Engineer/Architect

Soltech•Atlanta, GA

About The Position

Our client is seeking a highly skilled Senior Full-Stack AI Engineer/Architect to lead the design, development, and implementation of enterprise AI solutions that will power the next generation of intelligent banking experiences, advanced security capabilities, and operational support systems. This role is ideal for an experienced engineer who thrives on researching emerging AI technologies, evaluating their practical business applications, and independently recommending and delivering enterprise-scale AI solutions. The successful candidate will play a key role in shaping our client's AI strategy while remaining hands-on in designing and deploying production-ready systems. In addition to building enterprise AI platforms, this individual will collaborate closely with Product Management, Engineering, Sales, and strategic technology partners—including Microsoft and Intel—to support customer engagements through technical demonstrations, rapid proof-of-concepts (POCs), and client-specific AI pilots. The ideal candidate is equally comfortable architecting scalable AI platforms, rapidly prototyping innovative solutions, and presenting technical concepts to customers and executive stakeholders. This is an outstanding opportunity to help define the future of AI within a global technology organization while working with cutting-edge enterprise AI technologies.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field.
7+ years of professional software engineering experience, including designing and deploying production AI solutions.
Strong experience developing enterprise applications using React, Node.js, distributed systems, and microservices.
Hands-on experience integrating Large Language Models (LLMs) into enterprise applications.
Experience designing and implementing AI-enabled applications using modern AI frameworks and cloud platforms.
Strong understanding of machine learning concepts, data preprocessing, feature engineering, model evaluation, and AI system architecture.
Experience developing RESTful APIs and integrating enterprise systems.
Experience building and deploying containerized applications using Docker and Kubernetes.
Experience developing cloud-native applications within Microsoft Azure.
Proficiency with Git, CI/CD pipelines, and modern software engineering best practices.
Excellent analytical, problem-solving, communication, and presentation skills.
Demonstrated ability to independently research emerging technologies, recommend innovative solutions, and deliver production-ready implementations.
Ability to thrive in fast-paced environments that require rapid prototyping, experimentation, and execution.

Nice To Haves

Experience with Azure OpenAI Service, Azure AI Foundry, or comparable enterprise AI platforms.
Experience implementing Retrieval-Augmented Generation (RAG), vector databases, semantic search, or AI agent frameworks.
Experience with prompt engineering, model fine-tuning, and enterprise AI evaluation methodologies.
Experience supporting technical pre-sales engagements, customer workshops, solution demonstrations, or enterprise consulting initiatives.
Experience delivering AI proof-of-concepts and customer pilot programs.
Knowledge of federated learning and edge AI deployment.
Microsoft Azure, AWS, or Google Cloud AI certifications.
Experience with conversational AI, natural language processing (NLP), and computer vision.
Familiarity with AI governance, responsible AI practices, and regulatory compliance.
Experience working within financial services, banking, or other highly regulated industries is a plus.

Responsibilities

Lead the design, development, deployment, and continuous evolution of enterprise AI solutions.
Research emerging AI technologies, frameworks, and foundation models, evaluating their applicability to business and product initiatives.
Recommend AI architectures, technologies, and implementation strategies based on scalability, security, performance, and business value.
Serve as the technical subject matter expert for enterprise AI initiatives across Product, Engineering, and customer engagements.
Conduct proof-of-concepts and technical evaluations to validate new AI capabilities before production deployment.
Develop our client's private language model using enterprise-optimized foundation models designed for lightweight deployment, edge computing, and federated learning.
Design and implement AI architectures leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), semantic search, vector databases, and conversational AI.
Build scalable AI services that integrate seamlessly with enterprise applications using REST APIs, microservices, and distributed architectures.
Develop data ingestion, preprocessing, embedding, indexing, and model evaluation pipelines.
Fine-tune and optimize foundation models for enterprise-specific use cases.
Ensure AI solutions meet production standards for performance, scalability, reliability, and operational excellence.
Partner with Sales, Product, and Engineering teams to support technical pre-sales engagements and customer discovery sessions.
Design and build rapid proof-of-concepts and demonstrations showcasing enterprise AI capabilities.
Lead client-specific AI pilots, working directly with customers to validate business use cases and refine solutions based on real-world feedback.
Translate complex customer challenges into scalable AI solutions that progress from concept to production.
Present AI architectures, prototypes, and technical recommendations to customers, strategic partners, and executive leadership.
Collaborate with strategic technology partners, including Microsoft, Intel, and other industry leaders.
Partner with software engineers, data scientists, infrastructure teams, product managers, and operations teams to deliver integrated AI solutions.
Establish AI engineering best practices and mentor development teams on enterprise AI implementation.
Ensure AI solutions align with security, privacy, ethical AI, and regulatory compliance requirements.
Monitor production AI systems for performance, reliability, model drift, quality, and operational health.
Stay current with advancements in artificial intelligence, machine learning, cloud technologies, and enterprise architecture.