AI Engineer
Reindeer · Tel Aviv-Yafo, Tel Aviv District, Israel
- On site
- Full-time
- $150,000 / year
- Tel Aviv-Yafo, Tel Aviv District, Israel
Job highlights
- Develop AI agents for task automation.
- Automate enterprise document processing.
- Optimize large language models.
- Collaborate with cross-functional teams.
- Deploy and test AI solutions.
About the role
About the Role
We are a well-funded, early-stage startup, seeking a talented and motivated AI Engineer to join our team. The focus of this role is to develop advanced AI agents aimed at automating and optimizing low-skill human tasks, with a special emphasis on document processing, data extraction, text and email comprehension, and related workflows. You will be responsible for researching, designing, and deploying AI solutions that can replace repetitive manual tasks, improving efficiency and scalability for organizations.
Key Responsibilities
- AI Agent Development: Design, develop, and implement AI agents using best of breed LLMs to automate document processing tasks across enterprise workflows.
- Workflow Automation: Build machine learning pipelines to automate content-heavy processes for enterprise businesses.
- Model Optimization: Fine-tune and optimize pre-trained LLMs to accurately interpret, classify, and process large volumes of documents, improving speed and accuracy.
- Collaboration: Partner with product, engineering, and business teams to identify high-value automation opportunities and integrate AI-driven solutions into our operations.
- Evaluation & Testing: Develop metrics and conduct extensive testing to ensure reliability and efficiency of the AI systems in real-world scenarios.
Qualifications
- Experience: 5+ years of experience as a software engineer or data scientist building production systems.
- LLM & NLP Expertise: 2+ years of experience with large language models and NLP techniques.
- AI Agent Background: Background in developing AI agents for content-heavy tasks.
- LLM Vendor Experience: Experience with multiple LLM Vendors, RAG or Fine Tuning OSS or Commercial models.
- Technical Skills: Proficiency in Python and machine learning libraries such as TensorFlow or PyTorch.
- Deep Understanding: Deep understanding of LLM architectures and NLP applications like entity recognition, document classification, and summarization.
- Model Grounding & RAG: Experience with model grounding techniques, retrieval-augmented generation (RAG), and deploying robust AI models in production environments.
- Deployment Skills: Skills in designing and deploying AI models through APIs, containerization (Docker, Kubernetes), and cloud platforms (AWS, GCP, Azure).
- Model Lifecycle Management: Understanding of best practices for continuous monitoring, evaluation, and iterative improvement of deployed models.
Key skills/competency
- AI Engineer
- Large Language Models (LLMs)
- Natural Language Processing (NLP)
- Workflow Automation
- Machine Learning Pipelines
- Model Optimization
- Retrieval-Augmented Generation (RAG)
- Python
- TensorFlow/PyTorch
- Production AI Deployment
Skills & topics
- AI Engineer
- Artificial Intelligence
- Machine Learning
- Natural Language Processing
- LLM
- RAG
- Workflow Automation
- Python
- Software Engineering
- Data Science
How to get hired
- Tailor your resume: Highlight your experience with LLMs, NLP, and production system development.
- Showcase AI agent projects: Detail your work on document processing and automation in your application.
- Quantify your impact: Use metrics to demonstrate efficiency gains from your AI solutions.
- Prepare for technical interviews: Brush up on Python, ML libraries, and LLM concepts.
- Demonstrate collaboration skills: Be ready to discuss how you partner with teams.
Technical preparation
Practice Python and ML libraries.,Build sample AI agents for tasks.,Implement RAG for document retrieval.,Containerize and deploy models using Docker.
Behavioral questions
Describe a complex AI project you led.,How do you handle ambiguity in requirements?,Tell me about a time you improved efficiency.,How do you collaborate with non-technical teams?
Frequently asked questions
- What are the key responsibilities for an AI Engineer at Reindeer?
- As an AI Engineer at Reindeer, you will design, develop, and implement AI agents for document processing and workflow automation, optimize LLMs, and collaborate with teams to integrate AI solutions.
- What qualifications are essential for the AI Engineer role at Reindeer?
- Essential qualifications include 5+ years of software engineering/data science experience, 2+ years with LLMs and NLP, experience with AI agents, and proficiency in Python, ML libraries, and production deployment techniques like RAG.
- What experience with LLM vendors is Reindeer looking for?
- Reindeer seeks experience with multiple LLM vendors, RAG, and fine-tuning either open-source or commercial models.
- What technical skills are required for this AI Engineer position?
- Required technical skills include Python, machine learning libraries (TensorFlow, PyTorch), deep understanding of LLM architectures and NLP applications, and experience with RAG and production deployment.
- How does Reindeer approach AI model deployment and optimization?
- Reindeer emphasizes deploying models via APIs, using containerization (Docker, Kubernetes), cloud platforms (AWS, GCP, Azure), and continuous monitoring and evaluation for iterative improvement.
- What kind of impact can an AI Engineer make at Reindeer?
- An AI Engineer at Reindeer can significantly improve organizational efficiency and scalability by automating repetitive manual tasks through advanced AI solutions.
- Is there an opportunity to work with cutting-edge AI technologies at Reindeer?
- Yes, the role involves working with best-of-breed LLMs, advanced NLP techniques, RAG, and developing production-ready AI agents.