AI Engineer – Software Development Tools

HPE•San Juan, PR

2d•Hybrid

About The Position

We are seeking a Junior-to-mid-level AI Engineer to design, develop, and deploy AI-powered solutions that improve the productivity, quality, and efficiency of software development across the company. This role will work closely with software engineers, architects, DevOps teams, and engineering leadership to identify high-value developer workflows and transform them using Generative AI, LLMs, agents, RAG, and intelligent automation. The engineer will contribute across the full lifecycle—from identifying opportunities and prototyping AI solutions to integrating them into existing software development tools and workflows. The ideal candidate combines strong software engineering fundamentals with practical experience building AI/LLM applications and a passion for improving the developer experience.

Requirements

Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field.
2–6 years of software engineering or AI/ML engineering experience, with hands-on experience developing production-quality software.
Practical experience developing applications using Generative AI and Large Language Models (LLMs).
Strong programming skills in Python and/or JavaScript/TypeScript, with good understanding of software engineering principles.
Experience working with LLM APIs, prompt engineering, structured outputs, embeddings, RAG, or agent-based applications.
Experience developing and consuming REST APIs and microservices.
Strong understanding of software development lifecycle, source control, CI/CD, testing, debugging, and engineering workflows.
Familiarity with cloud platforms, containers, Kubernetes, or modern application deployment practices.
Ability to work effectively with software developers and translate engineering problems into practical technical solutions.
Strong analytical, problem-solving, and communication skills.

Nice To Haves

Experience building AI agents and multi-step AI workflows.
Experience with MCP, tool/function calling, agent frameworks, or AI orchestration frameworks.
Experience with vector databases, semantic search, knowledge graphs, or RAG architectures.
Experience applying AI to code generation, code review, unit-test generation, code coverage, debugging, build failure analysis, or CI/CD automation.
Familiarity with models from providers such as OpenAI, Anthropic, Google, Meta, or other leading LLM platforms.
Experience with AI evaluation frameworks and techniques for measuring LLM accuracy and reliability.
Knowledge of software engineering productivity metrics and developer experience.
Experience operating AI applications at scale, including observability, cost management, security, and performance optimization.

Responsibilities

Develop and integrate AI/GenAI capabilities into software development tools and workflows, including coding, code understanding, code review, testing, debugging, build, CI/CD, and release processes.
Build applications using LLMs, RAG, AI agents, tool calling, MCP, embeddings, and vector databases.
Develop AI-powered assistants and automation that help engineers understand code, diagnose failures, generate tests, analyze defects, and resolve development issues.
Integrate AI capabilities with existing engineering systems, APIs, source-code repositories, CI/CD pipelines, issue tracking, build systems, and developer environments.
Design and implement agentic workflows that can reason over engineering data and take appropriate actions through approved tools and APIs.
Evaluate different models, prompts, agents, and architectures for accuracy, latency, cost, reliability, and developer value.
Develop mechanisms for AI quality evaluation, including automated evaluation, human feedback, regression testing, and monitoring of AI-generated results.
Work with software development teams to understand pain points and translate them into practical AI-powered solutions.
Build scalable, secure, and maintainable AI services suitable for use by large engineering organizations.
Instrument AI applications to measure adoption, productivity impact, quality improvements, and business value.
Participate in design reviews, code reviews, architecture discussions, and engineering best-practice initiatives.
Stay current with rapidly evolving AI technologies and identify opportunities to incorporate relevant advances into internal developer tooling.