Job Title: Lead Engineer

Work Model: Contract-to-Hire – 3 months, Remote

Pay: 60-65/HR

Benefits: This position is eligible for medical, dental, vision, and 401(k).

Job Description:

The Lead Engineer, Information Systems – AI Solutions is responsible for providing technical leadership in the design, development, integration, implementation, and support of enterprise-level AI-powered solutions.

This position combines strong software engineering and systems integration expertise with practical experience in artificial intelligence, machine learning, large language models, document understanding, and automation. The Lead Engineer will guide technical design decisions, establish development practices, and collaborate with business stakeholders, engineers, and technology teams to translate business needs into secure, scalable, reliable, and maintainable solutions.

This is a technical leadership role and does not include formal people-management responsibilities. The Lead Engineer is expected to mentor other engineers, coordinate technical work, lead solution design, and promote engineering standards across projects.

Responsibilities

• Provide technical leadership for the design, development, implementation, integration, and ongoing support of enterprise AI-powered applications and services.
• Architect solutions that combine software applications, relational databases, APIs, document-processing technologies, machine learning models, and large language model services.
• Develop production-quality applications and services primarily using Python, with opportunities to work with C#/.NET, Java, or other object-oriented technologies.
• Lead the technical design of AI and machine learning solutions for use cases such as document classification, information extraction, image analysis, workflow automation, intelligent search, and other business-focused applications.
• Design and implement solutions using AI/ML frameworks and libraries such as scikit-learn, PyTorch, Hugging Face Transformers, spaCy, or comparable technologies.
• Integrate large language model platforms and AI services into enterprise applications and workflows.
• Apply prompt-engineering techniques, structured output schemas, validation methods, and optimization practices to improve the reliability, efficiency, and scalability of AI solutions.
• Design and support OCR and document-understanding pipelines using appropriate cloud services, open-source technologies, vision models, or comparable solutions.
• Apply machine learning lifecycle concepts, including training-data preparation, model evaluation, confidence thresholds, human review, and feedback loops.
• Design integrations between internal and external systems using APIs, REST services, JSON, XML, and batch-processing methods.
• Develop and maintain relational database structures, queries, data-access components, and data-processing workflows.
• Evaluate and apply embedding models, vector similarity search, and retrieval-augmented generation patterns when appropriate for business and technical requirements.
• Lead technical discovery and collaborate with stakeholders, business teams, product teams, and senior leadership to understand requirements and translate them into actionable technical designs.
• Guide engineers through solution architecture, implementation decisions, code reviews, troubleshooting, testing, deployment, and operational support.
• Mentor engineers and promote consistent engineering practices without serving as a formal people manager.
• Lead troubleshooting and root-cause analysis for complex or high-priority production issues, minimizing disruption to business operations.
• Ensure solutions meet organizational expectations for security, scalability, performance, reliability, maintainability, and responsible use of AI.
• Develop code, automated tests, technical documentation, system designs, implementation procedures, support materials, and operational workflows in a fast-paced environment.
• Use version-control and collaborative development platforms such as Git, Azure DevOps, GitLab, GitHub, or comparable tools.
• Contribute to or lead the development of continuous integration and continuous delivery pipelines.
• Support solutions deployed in containerized environments using technologies such as Docker, Kubernetes, or comparable platforms.
• Contribute to distributed task-processing and queue-based architectures using technologies such as Celery, RabbitMQ, or comparable platforms.
• Monitor emerging developments in enterprise software engineering, artificial intelligence, machine learning, and information systems, and recommend improvements where they provide measurable business value.
• Support technology projects by identifying technical dependencies, risks, implementation considerations, and delivery milestones.
• Perform other duties as assigned.

Required Qualifications

• Bachelor’s degree in Information Technology, Computer Science, Software Engineering, Data Science, or a related field, or equivalent practical work experience.
• 5+ years of relevant corporate software engineering, systems integration, information systems, or application-development experience. Experience should include work in a business or production environment rather than exclusively academic research or coursework.
• Strong Python programming skills.
• Exposure to C#/.NET, Java, or another object-oriented programming language.
• Experience designing, querying, or integrating with relational databases.
• Experience integrating systems using APIs, REST, JSON, XML, and batch-processing approaches.
• Experience developing well-documented, maintainable code and executing automated tests in a fast-paced environment.
• Experience using version-control and development collaboration tools such as Git, Azure DevOps, GitLab, GitHub, or comparable platforms.
• Experience or practical exposure to AI/ML frameworks and libraries such as scikit-learn, PyTorch, Hugging Face Transformers, spaCy, or comparable technologies.
• Familiarity with large language model APIs and concepts such as prompt engineering, structured outputs, validation, and optimization.
• Exposure to OCR, computer vision, or document-understanding pipelines.
• Understanding of machine learning lifecycle concepts, including training-data preparation, model evaluation, confidence thresholds, weak supervision, and active learning.
• Strong analytical and problem-solving skills, including the ability to troubleshoot complex application, integration, data, and AI-related issues.
• Strong written and verbal communication skills, with the ability to collaborate effectively with technical teams, business stakeholders, and senior leaders.
• Ability to translate business requirements into technical designs and explain complex technical concepts to nontechnical audiences.
• Demonstrated ability to provide technical direction, mentor engineers, and influence engineering decisions without formal management authority.
• Strong organizational skills and the ability to manage competing priorities across multiple initiatives.

Preferred Qualifications

• Experience developing AI-powered solutions in a corporate or enterprise environment.
• Experience with document classification, information extraction, image analysis, intelligent document processing, or related AI use cases.
• Familiarity with embedding models, vector databases or vector similarity search, and retrieval-augmented generation patterns.
• Exposure to distributed task-processing or queue-based architectures.
• Exposure to container technologies and orchestration platforms such as Docker, Kubernetes, or comparable technologies.
• Experience contributing to or building CI/CD pipelines.
• Experience with cloud technologies, enterprise security practices, system monitoring, and application performance optimization.
• Relevant certifications in cloud computing, software engineering, data, AI, security, or related technologies.

Leadership Expectations

The Lead Engineer serves as a technical leader rather than a formal people manager. Leadership responsibilities include:

• Establishing technical direction and engineering standards.
• Facilitating architecture and design decisions.
• Mentoring and supporting other engineers.
• Coordinating technical work across teams and projects.
• Reviewing designs and code for quality, security, maintainability, and performance.
• Communicating technical risks, tradeoffs, and recommendations to stakeholders.
• Promoting responsible, reliable, scalable, and cost-effective use of AI technologies.