AI Engineer – Software Development Tools (Generative AI)

Location: Hybrid, with an average requirement of two days per week working from an HPE office. Relocation support is provided to eligible candidates.

Company

hirify.global is a global edge-to-cloud technology company helping organizations connect, protect, analyze, and use data and applications.

What you will do

• Design, develop, and deploy AI-powered capabilities for software development tools and workflows.
• Build applications using generative AI, LLMs, RAG, AI agents, tool calling, MCP, embeddings, and vector databases.
• Develop assistants for code understanding, code review, test generation, debugging, defect analysis, and build or CI/CD failure diagnosis.
• Integrate AI services with APIs, source-code repositories, CI/CD pipelines, issue trackers, build systems, and developer environments.
• Evaluate models, prompts, agents, and architectures for accuracy, latency, cost, reliability, adoption, and developer value.
• Build scalable, secure, observable, and maintainable AI services for large engineering organizations.

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 developing production-quality software.
• Practical experience with generative AI, LLM applications, LLM APIs, prompt engineering, structured outputs, embeddings, RAG, or agent-based applications.
• Strong programming skills in Python and/or JavaScript/TypeScript, with knowledge of software engineering principles.
• Experience with REST APIs, microservices, software development lifecycle practices, source control, CI/CD, testing, and debugging.
• Familiarity with cloud platforms, containers, Kubernetes, or modern application deployment practices.

Nice to have

• Experience building AI agents, multi-step workflows, MCP integrations, tool or function calling, and AI orchestration solutions.
• Experience with vector databases, semantic search, knowledge graphs, RAG architectures, or AI evaluation frameworks.
• Experience applying AI to code generation, code review, unit-test generation, code coverage, debugging, build failure analysis, or CI/CD automation.
• Experience operating AI applications at scale, including observability, cost management, security, and performance optimization.

Culture & Benefits

• Hybrid work model with flexibility to manage professional and personal needs.
• Health and wellbeing benefits supporting physical, financial, and emotional wellbeing.
• Personal and professional development programs supporting career growth and skill development.
• Inclusive workplace committed to accessibility and equal employment opportunity.