Overview
Curate Partners works with leading enterprises across healthcare, financial services, life sciences, and digital industries to build AI systems that create measurable impact. As an AI engineer, you’ll design and deploy intelligent models that transform data into outcomes — applying large language models (LLMs), workflow automation, and secure integrations to help organizations move faster and smarter.
Key responsibilities
- Design, build, and optimize AI models and end-to-end workflows for real-world business applications.
- Apply and fine-tune large language models (LLMs) to address complex operational and analytical challenges.
- Integrate AI systems across APIs, on-premise, and cloud-based environments for reliable production deployment.
- Implement authentication, authorization, and security controls across all AI pipelines and workflows.
- Develop automated testing, validation, and monitoring systems to ensure model performance and trustworthiness.
- Collaborate with product managers, data scientists, and engineers to align AI initiatives with business goals.
- Stay current with advancements in AI frameworks, agent architectures, and workflow automation.
Required skills
- Proficiency in Python and leading ML frameworks such as PyTorch, TensorFlow, or Hugging Face.
- Strong understanding of machine learning, natural language processing, and large language models.
- Experience in prompt engineering, model optimization, and fine-tuning.
- Knowledge of system design, API integration, and secure data architecture.
- Ability to deploy, manage, and monitor AI systems in production environments.
- Strong analytical, troubleshooting, and communication skills.
Ideal candidate profile
You’re an engineer who thinks strategically and codes with precision. You excel at bridging technical design with business impact, creating solutions that scale securely and intelligently. You stay curious, adapt quickly to emerging AI technologies, and thrive in collaborative environments where innovation meets execution.
Why work with Curate
At Curate Partners, we embed fractional leaders, technical experts, and AI specialists directly into client teams to accelerate progress. Our network of subject matter experts (SMEs) drives both strategy and implementation — helping organizations navigate data transformation, AI adoption, and scalable automation with confidence.
FAQs
What types of AI projects does the AI Engineer work on?
The AI Engineer works on enterprise AI projects involving large language models, workflow automation, predictive analytics, document intelligence, customer support automation, and secure AI integrations. Projects typically support industries such as healthcare, financial services, life sciences, and digital products.
Which tools and technologies are most important for this AI Engineer role?
The most important tools for this AI Engineer role include Python, PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Docker, Kubernetes, AWS, Azure, and GCP. Experience with APIs, vector databases, prompt engineering, and model monitoring is also highly valuable.
How much experience with large language models is expected?
Candidates should have hands-on experience using, fine-tuning, or integrating large language models in production or prototype environments. Knowledge of prompt engineering, retrieval-augmented generation (RAG), embeddings, and evaluation methods is important for success in this position.
What does the interview process for the AI Engineer position include?
The interview process typically includes an initial recruiter discussion, a technical interview, a problem-solving or coding exercise, and conversations with business or technical stakeholders. Candidates may be asked about AI architecture, Python development, cloud deployment, and real-world AI use cases.
How is success measured for an AI Engineer at Curate Partners?
Success is measured by the ability to design scalable AI systems, improve operational efficiency, reduce manual work, and deliver measurable business outcomes. Strong collaboration, reliable deployments, and secure implementation practices are also key performance indicators.
What level of cloud experience is required for this AI Engineer role?
Candidates should have experience deploying and managing AI systems in AWS, Azure, Google Cloud Platform, or hybrid cloud environments. Knowledge of containerization, Kubernetes, APIs, and production monitoring tools is highly beneficial.
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