FluidWeb

Middle+ AI Engineer
As an AI Engineer, you will work within a team responsible for taking AI/ML solutions from initial concept to full production implementation. This role requires creativity and analytical skills to understand business challenges, propose AI-enabled solutions, and bring them to life through robust system design, modeling, and coding. You will collaborate cross-functionally to ideate and prototype new AI products. Your models, architectures, and software contributions will power innovative AI systems that solve complex problems at scale. To excel in this role, you will need hands-on experience rapidly developing and deploying ML algorithms alongside strong software engineering abilities to build reliable and maintainable AI infrastructure.

This is a remote/telecommuter position, working from home.

 

What you’ll do:

• Develop comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.
  
   

What you’ll bring:

Minimum Qualifications

• Bachelor's Degree in Computer Science, Statistics, Mathematics, or a related field, or 2+ years of experience in a related, professional IT/analytics position.
• Minimum of 3 years of industry experience in developing, deploying, and maintaining AI or ML systems. Up to two years of industry experience may be substituted with an AI centered Master’s/PhD or AI Engineering certifications.
  
   

Preferred Qualifications

• At least 4 years of experience in developing deep learning models using TensorFlow, PyTorch, MLX, JAX, or other modern deep learning frameworks. Previous experience with older libraries like Theano or Caffe is also accepted. (May supplement with graduate level education or research experience).
• Knowledge of and experience in implementing ethical AI practices, with at least 2 years spent working on projects that require explainable AI, fairness, and bias mitigation.
• Minimum of 3 years of proficiency in utilizing advanced prompt engineering techniques like General Knowledge Prompting and ReAct. Adept at using libraries like LangChain or OpenPrompt for complex projects. Possess the capability to mitigate prompt injection attacks and use tools like Guardrails or PromptInject for securing prompt engineering pipelines.
• Minimum of 3 years of experience in successfully productionizing AI models, including constructing scalable data pipelines and establishing robust monitoring systems.
• 3 or more years working within Agile-like teams and environments.
• Knowledge, Skills, and Abilities
• Experience developing deep learning architectures such as CNNs, transformers, GANs, LSTMs, GNNs, Autoencoders, Diffusion Models, and Neural Ordinary Differential Equations (NODEs).
• Proficiency in debugging AI systems and enhancing performance through hyperparameter tuning and similar techniques.
• Proven experience in productionizing models by constructing scalable data pipelines, low-latency services, and robust monitoring.
• Familiarity with software design patterns, microservices, distributed computing, container orchestration, and other relevant architectures.
• Proficient software engineering skills as well as skills building secure, stable software systems at scale.
• Proficiency in developing and optimizing ML solutions using languages like Python, and libraries such as NumPy, Pandas, Matplotlib and scikit-learn.
• Familiarity working with traditional ML lifecycles.
• Familiarity with building minimal interfaces to interact with AI products, e.g., Streamlit or Shiny.
• Familiarity with ethical AI practices including explainable AI, fairness, and mitigation of bias/hallucinations.
• Strong communication, collaboration, and mentorship skills.
• Growing ability to articulate the technical details and tradeoffs of AI solutions to non-technical stakeholders in a clear and concise manner.