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AI Engineer (Multi-Agent LLM Systems)

We are seeking an experienced AI Engineer specializing in multi-agent LLM systems. This role requires a deep understanding of constructing, training, and deploying AI agents that can dynamically execute Python-based functions in response to specified needs. As part of our team, you’ll develop agents that can autonomously select and perform functions based on contextual requirements and work seamlessly within a robust AWS infrastructure.

Responsibilities:

  • Design, implement, and deploy AI agents that can autonomously learn to select and execute custom functions using Python.
  • Train agents to recognize contextual prompts and determine the necessary function to perform specific tasks, optimizing for accuracy and efficiency.
  • Develop frameworks for dynamic task assignment within multi-agent systems, enabling agents to coordinate and collaborate effectively on complex workflows.
  • Deploy and manage the multi-agent system within the AWS environment (e.g., Lambda, SageMaker, EC2), ensuring scalability and resilience.
  • Work closely with cross-functional teams to define function requirements and develop APIs or connectors for seamless task execution.
  • Implement strategies to improve model performance in function execution, covering error handling, retry mechanisms, and adaptability to changing requirements.
  • Document designs, workflows, and function-specific agent behaviors to support team collaboration and product transparency.
  • Proven Experience: 1+ years working with LLMs, with a strong focus on developing function-driven multi-agent systems.
  • Technical Skills: Expertise in Python and experience with custom function execution within LLM frameworks; skilled in prompt engineering, model fine-tuning, and dynamic function mapping. Experience in OpenAI Swarm and other relevant tech is a plus.
  • AWS Expertise: Demonstrated ability to deploy and manage models within AWS, using Lambda, SageMaker, EC2, and other essential AWS tools.
  • Web3 Knowledge: Familiarity with blockchain principles and web3 technologies is a plus.
  • Problem-Solving Skills: Experience developing autonomous, adaptable agents capable of selecting and performing tasks based on contextual cues.
  • Communication: Strong documentation skills for cross-functional alignment and clear communication of complex workflows.

Preferred Qualifications:

  • Experience with reinforcement learning or decision-making models to enhance autonomous task selection.
  • Familiarity with Docker and Kubernetes for robust, containerized deployments.
  • Previous experience in web3 or blockchain environments, including working with smart contracts or decentralized applications.

  • HMO
  • Work From Home
  • Tokens
  • Leave Credits
  • Training & Development


Average salary estimate

$90000 / YEARLY (est.)
min
max
$70000K
$110000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About AI Engineer (Multi-Agent LLM Systems), Sovrun

Are you an innovative AI Engineer with a passion for multi-agent LLM systems? At our company, we’re looking for someone just like you to join our dynamic team. As an AI Engineer, you'll have the exciting opportunity to design, implement, and deploy intelligent agents that can learn to execute Python-based functions autonomously. Your expertise will be crucial in constructing agents that accurately respond to contextual prompts, optimizing their performance to handle complex workflows seamlessly. You will manage and deploy these agents within a state-of-the-art AWS infrastructure, utilizing tools like Lambda, SageMaker, and EC2. Your collaborative spirit will shine as you work cross-functionally to define requirements, develop APIs, and enhance overall system efficacy. If you have at least a year of relevant experience with LLMs and a solid skillset in Python, this role is tailored for you. Bring your knowledge of reinforcement learning, Docker, and perhaps even blockchain principles to our vibrant, web3-oriented company. With perks like the option to work from home, leave credits, and extensive training and development opportunities, you’ll thrive in an environment that supports both growth and creativity. Join us in pushing the boundaries of AI technology today!

Frequently Asked Questions (FAQs) for AI Engineer (Multi-Agent LLM Systems) Role at Sovrun
What are the primary responsibilities of an AI Engineer specializing in multi-agent LLM systems at our company?

As an AI Engineer focusing on multi-agent LLM systems, your main responsibilities include designing and deploying AI agents that autonomously select and execute custom functions using Python. You'll train these agents to understand contextual prompts, develop frameworks for task assignment, and ensure seamless management within our AWS infrastructure. Additionally, you will document workflows and collaborate closely with cross-functional teams to define function requirements.

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What qualifications are required for the AI Engineer role at our company?

To be considered for the AI Engineer position, candidates should have at least 1 year of experience working with LLMs, specifically in developing multi-agent systems. A strong proficiency in Python, along with experience in prompt engineering, model fine-tuning, and dynamic function mapping, is essential. Familiarity with AWS tools and web3 technologies is a plus that will enhance your application.

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How does the AI Engineer at our company collaborate with other teams?

In the role of AI Engineer specializing in multi-agent LLM systems, collaboration is key. You'll work with cross-functional teams to define functional requirements, develop APIs for task execution, and ensure that agents effectively communicate. Your ability to document workflows and agent behaviors will also support clear communication and alignment across teams, driving the overall success of our projects.

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What technologies and tools should an AI Engineer be familiar with for this position?

Candidates should be well-versed in Python for creating and executing functions. Familiarity with LLM frameworks, reinforcement learning, AWS services (like Lambda, SageMaker, and EC2), Docker, and Kubernetes will significantly boost your candidacy for the AI Engineer role. Knowledge of blockchain and web3 technologies is also beneficial.

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What benefits can an AI Engineer expect while working at our company?

As an AI Engineer with our company, you can look forward to a flexible work environment, including the option to work from home. In addition to competitive compensation, we offer leave credits, training and development opportunities, and the potential for tokens resulting from your contributions to innovative projects.

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Common Interview Questions for AI Engineer (Multi-Agent LLM Systems)
Can you explain how you would design an autonomous AI agent to execute tasks based on contextual cues?

When answering this question, discuss your approach to incorporating contextual awareness in AI agents. Emphasize your methodology for training the model, the importance of data quality, and how you would use reinforcement learning to improve task selection over time.

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What experience do you have with deploying machine learning models in AWS?

Highlight any past projects where you deployed models using AWS services. Detail the specific tools you used, such as Lambda and SageMaker, and mention how you ensured the scalability and resilience of your deployments.

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Describe your familiarity with multi-agent systems and their advantages.

Be prepared to articulate your understanding of multi-agent systems, focusing on how they facilitate collaboration and coordination among agents. Discuss specific benefits such as improved efficiency in handling complex workflows and enhanced adaptability in dynamic contexts.

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How do you ensure the accuracy and efficiency of function execution in AI agents?

Discuss your strategies for optimizing function execution, which might include implementing error handling, retry mechanisms, and continuous monitoring of agent performance. Mention any evaluation metrics you consider to ensure the agents perform optimally.

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What is your approach to documentation within AI engineering projects?

Describe how you prioritize clear and comprehensive documentation. Explain how good documentation fosters effective communication within cross-functional teams and supports transparency in workflows, thus ensuring all stakeholders understand the project vision and execution.

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Can you provide an example of a challenging problem you faced while working on an AI project?

Share a specific challenge, whether technical or collaborative, that you encountered in a past project. Elaborate on the steps you took to overcome it, the lessons learned, and how this experience has prepared you for the AI Engineer role.

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What are your thoughts on the future of multi-agent LLM systems?

Articulate your insights into the evolving landscape of AI, specifically focusing on advancements in multi-agent systems. Discuss emerging trends, potential applications, and your vision for how these systems will transform industries.

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How do you stay updated with the latest advancements in AI and machine learning?

Discuss your commitment to continuous learning. Mention sources such as journals, online courses, and professional communities you engage with to stay abreast of emerging technologies and best practices in AI.

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What experience do you have with documentating and sharing your AI engineering work?

Share your practices for documenting your work, such as writing technical specifications or sharing through platforms like GitHub. Discuss how you make these resources accessible to facilitate collaboration and knowledge sharing.

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Have you worked with containerization technologies like Docker or Kubernetes?

If applicable, discuss your experience with Docker and Kubernetes, focusing on how you've utilized these technologies for deploying AI models. Provide examples of specific projects where you implemented containerization to enhance stability and scalability.

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DATE POSTED
January 25, 2025

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