AI Software Engineer (Applied AI)

TL;DR

AI Software Engineer (Applied AI): Design, build, and support production AI capabilities for LLM-powered automation and decision support with an accent on RAG, agentic workflows, evaluation/guardrails, and reliable cloud delivery. Focus on turning AI into dependable products, services, and automation by integrating model APIs with enterprise data and internal systems.

Company

hirify.global builds and supports PlayStation experiences and digital commerce capabilities through applied data science and ML engineering.

What you will do

• Implement applied AI services, workflows, and reusable components for LLM-powered automation, retrieval, tool use, summarization, classification, and decision support.
• Collaborate with operations, product, data, risk, and engineering partners to prototype AI solutions, measure outcomes, and move proven capabilities into production.
• Build agentic workflows and integrations using tool/function calling, structured outputs, workflow state, internal APIs, and human review patterns.
• Develop retrieval and knowledge systems using embeddings, vector databases, hybrid search, reranking, citations, access controls, and freshness strategies.
• Create and maintain evaluation suites, regression tests, prompt/model versioning, trace analysis, guardrails, policy checks, PII handling, hallucination mitigation, and operational monitoring.
• Deliver production AI engineering with scalable APIs, microservices, event-driven workflows, and AWS-based deployment with CI/CD, observability, and runbooks.

Requirements

• 3+ years (or equivalent) of professional software engineering experience.
• Hands-on experience building AI or generative AI features that connect model APIs to business workflows, data, documents, or internal services.
• Strong coding skills in Python and/or Java, including API development, testing, debugging, asynchronous processing, and maintainable service design.
• Experience with AWS (or equivalent cloud services).
• Familiarity with RAG and retrieval systems: embeddings, chunking, indexing, retrieval strategies, vector/hybrid search, reranking, citations, and vector stores (e.g., OpenSearch, Pinecone, Weaviate, Redis, pgvector, Azure AI Search).
• Experience with AI orchestration and workflow tools (e.g., LangChain, LangGraph, LlamaIndex, Semantic Kernel, OpenAI Agents SDK, N8N, AWS Bedrock Agents/Knowledge Bases) plus structured outputs and tool/function calling.

Nice to have

• Degree/qualification in Computer Science, Software Engineering, or a related technical field.
• Familiarity with Model Context Protocol (MCP) or similar patterns for connecting AI applications to enterprise tools and workflows.
• Experience with multimodal AI systems (text/image/document/audio/video) and related pipelines (e.g., OCR/document understanding, content moderation).
• Commerce or trust domain experience (fraud, payments, risk, customer support, marketplace operations, trust & safety, content operations, or digital commerce processes).

Culture & Benefits

• Applied AI engineering focus: production reliability, safety, and measurable business outcomes.
• Cross-functional collaboration across engineering, operations, data, risk, and product stakeholders.
• Emphasis on evaluation, monitoring, and guardrails to keep AI systems auditable and controlled.
• Background checks conducted at offer stage for new employees.

Hiring process

• Interview process to assess applied AI engineering experience and production readiness.
• Background checks at the offer stage.