Time since we first saw it
Day 12
Similar jobs usually come down between day 4 and day 77.
From 51 similar postings that came down. Coming down is not always a hire.
Reads like an AI Engineer role: building products on existing AI models.
Work setup in the employer’s posting: TELECOMMUTEFrom the employer’s posting
Design applications using large language models and generative AI
Implement prompt engineering strategies and RAG architectures
Build frameworks to evaluate model performance, safety, and alignment
Create interfaces between generative AI models and production systems
Develop responsible AI mechanisms and guardrails for deployment
Design applications utilizing large language models (LLMs) and generative AI technologies
Implement prompt engineering strategies and Retrieval-Augmented Generation (RAG) architectures
Build frameworks to evaluate model performance, safety, and alignment
Create interfaces between generative AI models and production systems
Develop mechanisms for responsible AI and guardrails for deployment
Background in developing applications with generative AI models
Expertise in prompt engineering and RAG architecture
Proficiency in Python
Knowledge of modern AI/ML frameworks (e.g., LangChain)
Familiarity with vector databases and embedding models
We are seeking a Senior AI/ML Engineer who will be responsible for designing, implementing, and optimizing generative AI solutions for applications. Responsibilities Design applications utilizing large language models (LLMs) and generative AI technologies Implement prompt engineering strategies and Retrieval-Augmented Generation (RAG) architectures Build frameworks to evaluate model performance, safety, and alignment Create interfaces between generative AI models and production systems Develop mechanisms for responsible AI and guardrails for deployment Stay updated on advancements in generative AI research and best practices Requirements Background in developing applications with generative AI models Expertise in prompt engineering and RAG architecture Proficiency in Python, with added value for experience in asynchronous applications Knowledge of modern AI/ML frameworks (e.g., LangChain) Familiarity with vector databases and embedding models Understanding of responsible AI practices and mitigation strategies for hallucinations Competency in working with cloud-based AI services (e.g., OpenAI API, Azure AI) Nice to have Background in developing solutions for specific industries such as life sciences, finance, or insurance Familiarity with LLM tracing, evaluation datasets, and experiment management Understanding of techniques for evaluating RAG systems or other LLM architectures Skills in automated prompt optimization tools (e.g., dspy) Expertise in creating rapid prototypes
Actively hiringFirst seen by us 12 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 12
Similar jobs usually come down between day 4 and day 77.
From 51 similar postings that came down. Coming down is not always a hire.
673 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordCheck the employer’s posting for the current role and application details.
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Yes. We’ve followed one other Senior AI/ML Engineer job there since November 2026. It is still up.
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
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