Loading...
Mid LevelFull-timeTypeScriptPythonC#RailsAWSGCPAzurePyTorchCI/CDGit
Salary Range
$45,900 - $62,100 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are looking for an AI Engineer to join Hitachi Digital Services' CAAI (Center for AI Architecture and Implementation). This role involves designing, building, and operating AI capabilities that can be safely embedded into real delivery, from LLM-enabled workflows to RAG and agent-based systems.
Key Responsibilities
- βEngineer AI solutions end-to-end: data preparation, model or LLM integration, evaluation, deployment, and operational monitoring.
- βTranslate business and delivery needs into working AI designs, collaborating with architects, platform engineers, and client stakeholders.
- βContribute reusable assets: reference implementations, patterns, templates, internal libraries, and delivery playbooks.
- βBuild with production discipline: testing, version control, CI/CD alignment, and pragmatic performance/scalability considerations.
- βDeliver production-grade AI components independently, mentor juniors informally, and improve CAAIβs reusable assets.
- βOwn small-to-mid AI workstreams: from design through release and operational handover.
- βEngineer RAG and agent-based capabilities with clear evaluation, failure-mode handling, and measurable outcomes.
- βPartner with solution architects to integrate AI into broader delivery.
Requirements
- βStrong software engineering in Python (and/or TypeScript/C#), building API-first services and integrating AI capabilities into real application layers.
- βPractical agent/LLM application engineering: prompt/tool design, orchestration patterns, and integrating with agent frameworks.
- βRetrieval and knowledge patterns: embeddings, vector search, indexing, and using fit-for-purpose stores to support RAG-style systems and memory.
- βMLOps/LLMOps hygiene: reproducibility and lifecycle management, using tools such as MLflow.
- βWorking knowledge of ML/GenAI concepts and common frameworks.
- βFamiliarity with cloud platforms (AWS, Azure, or GCP) and the practicalities of deploying AI services.
- βClear communication and a habit of writing things down.
- βDegree (or equivalent experience) in CS/Engineering/Maths; strong fundamentals and programming ability.
- βHands-on delivery experience with GenAI, including RAG and agent-based systems.
- βSolid cloud engineering habits: services integration, deployment patterns, and reliability basics.
