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Socotec

Applied AI Engineer

Socotec, Brooklyn, New York, United States,


Job Description

As an Applied AI Engineer at SOCOTEC, you will be responsible for designing, developing, and deploying AI models that enhance our chatbot program and other AI-driven solutions. You will collaborate with cross-functional teams, including data scientists, software engineers, and business stakeholders, to integrate advanced AI technologies that address real-world challenges within our industry. You will focus on building scalable, efficient, and accurate machine learning pipelines while staying at the forefront of the latest AI advancements.

Developing AI Models: Design and implement machine learning models tailored to natural language processing (NLP) and other AI applications within our chatbot and enterprise systems.

Collaborating with Cross-Functional Teams: Work closely with product managers, data scientists, and software engineers to translate business requirements into intelligent AI-driven solutions.

Building and Maintaining AI Pipelines: Create and optimize end-to-end machine learning pipelines, from data preprocessing to model deployment, ensuring scalability and performance.

Optimizing Model Performance: Continuously fine-tune and improve the accuracy, speed, and robustness of AI models, leveraging techniques like hyperparameter tuning and model optimization.

Implementing Best Practices in AI/ML Development: Ensure the reliability and quality of AI models by following best practices for training, testing, and model evaluation.

Integrating AI Solutions: Work closely with backend engineers to integrate AI models into production environments using tools like LangChain and cloud services.

Staying Updated with AI/ML Trends: Keep up with the latest developments in AI, machine learning, and natural language processing, applying new techniques to improve our technology stack.

Documentation: Provide detailed documentation of AI workflows, models, and algorithms to facilitate collaboration and ensure long-term maintainability