Software Engineer, AI & Software Engineering (temp-to-perm)

Aspen Power•New York, NY

12h•Onsite

About The Position

Reporting to the Lead Software Engineer, AI & Software Engineering, the role will help design, build, and maintain internal automation and agentic AI systems that improve how the organization works. This is a hands-on role for an emerging engineer or self-taught automation builder who already has strong software fundamentals and can reason through technical trade-offs, while working closely with project management and software engineering. The candidate should have a solid foundation in programming, APIs, basic infrastructure concepts, and structured technical problem-solving. Guidance will focus on Aspen's systems, business context, project priorities, higher-level design decisions, and how to choose the right approach for production workflows. Practical experience with automation, OpenClaw, Hermes agents, LLM APIs, or similar agent frameworks is more important than a traditional credential alone. This is a two-month contractor contract role (40 hours/week), and then transition to full-time employment upon good performance.

Requirements

Must be passionate about AI, engineering, automation, and practical problem-solving.
The candidate should be curious, coachable, comfortable learning quickly, and able to propose new approaches, understand solution trade-offs, and execute within a project-managed software development environment.
Strong software fundamentals: object-oriented design, version control, testing, debugging, and clean, maintainable code.
Ability to reason through technical trade-offs, ask good questions, compare implementation options, and justify the approach taken.
Strong proficiency in Python as the primary development language, with basic familiarity in C#, JavaScript/TypeScript, or similar languages to understand existing code, integrations, and core programming fundamentals.
Hands-on experience building AI-assisted or automation-focused applications using LLM APIs, LangChain, OpenClaw, Hermes Agent, agent frameworks, workflow tools, or similar automation systems.
Practical understanding of APIs, web services, authentication, data flow, deployments, and cloud concepts such as Azure or AWS; deep cloud specialization is not required.
Evidence of staying current with the AI and automation ecosystem, such as shipped projects, internal tools, prototypes, OpenClaw or Hermes agent work, open-source contributions, or a portfolio of practical builds.
Detail-oriented and organized, able to break complex problems into actionable steps and execute assigned work within a collaborative software development workflow.
Clear communication skills, with the ability to explain technical trade-offs to project managers and software engineers.
Collaborative and engaging team member who partners well with others
Strong team orientation and demonstrated client management skills
Results-driven, with a strong work ethic and commitment to excellence
Executive presence, outgoing with strong communication and presentation skills
Builds trust by demonstrating high ethical standards and personal integrity
Ability to adapt and learn quickly
Analytical problem solver who views obstacles as challenges
Self-motivated builder who works well with direction, project priorities, and engineering feedback.

Nice To Haves

A Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a related technical field is helpful, but equivalent self-taught and practical engineering experience will be strongly considered.

Responsibilities

Work with project management and software engineering to identify automation and AI-driven process improvement opportunities, then help carry solutions from discovery through delivery.
Design and build agentic workflows using tools such as OpenClaw, Hermes agents, LLM APIs, or similar frameworks to streamline repetitive tasks and accelerate decision-making.
Develop Python-based automation programs, scripts, and integrations to support data flow, validation, reporting, and business operations.
Build and support internal tools as web applications, serverless functions, scripts, and APIs, with attention to reliability, maintainability, and practical deployment needs.
Evaluate technical approaches, infrastructure choices, and implementation trade-offs with the engineering team; explain why one approach is better than another for the problem at hand.
Measure, quantify, and report the time savings, performance gains, and decision-making improvements delivered by implemented solutions.
Document technical designs, agent workflows, implementation decisions, and best practices so solutions remain maintainable and easy to build on.