AI Engineer

PwC•Tampa, FL

1d•$50,500 - $112,500

About The Position

As an AI Engineer, you will be at the forefront of transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Within our Internal Firm Services practice, you will apply data, algorithms, and software engineering to build and deploy software and platform systems that create Artificial Intelligence and Machine Learning-based solutions at scale. Your work will involve designing AI systems, data wrangling, and software implementation to enable the AI models to be useful and scalable. As an Associate, you will focus on learning and contributing to projects while developing your skills and knowledge to deliver quality work. You will engage with different stakeholders to build meaningful connections, learn how to manage and inspire others, and grow your personal brand by deepening your technical knowledge of firm services and technology resources. In increasingly complex situations, you will build acumen to anticipate the needs of your teams and internal stakeholders, embrace ambiguity, ask questions, and use these challenges as opportunities for growth. In this role, you will take ownership and consistently deliver quality work that drives value for our clients and success as a team. You will be part of a dynamic environment where every experience is an opportunity to learn and grow, opening doors to more opportunities within the firm.

Requirements

At least a Bachelor's degree or, in lieu of a degree, demonstrating in addition to the minimum years of experience required for the role, three years of specialized training and/or progressively responsible work experience in Engineering with AI and Machine Learning for each missing year of college is required
At least 1 years of experience
Proficiency in Python and TensorFlow for AI projects
Utilizing machine learning libraries like Scikit-Learn for data analysis
Engaging in complex data analysis and pattern recognition
Implementing AI solutions using open-source software
Applying natural language processing techniques in real-world applications

Nice To Haves

In at least one of the following fields of study: Computer and Information Science, Computer Engineering, Computer Management, Management Information Systems, Information Technology
At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials
Building and orchestrating AI agent workflows using frameworks such as LangGraph to automate multi-step reasoning, integrate tools and APIs, and deliver scalable, context-aware solutions
Applying generative AI techniques, including prompt engineering, LLM evaluation, and fine-tuning, to develop production-ready applications powered by foundation models
Developing automated evaluation frameworks, including LLM-as-judge pipelines, regression testing, and adversarial benchmarking, to assess reasoning, tool-calling reliability, and output groundedness
Optimizing open-weight language models, including LLaMA, Mistral, and Gemma, for local and cloud deployment using quantization, inference acceleration, and model-routing techniques
Designing agent harnesses and implementing context engineering, memory management, retry logic, and structured output validation to support reliable, multi-step AI workflows

Responsibilities

Designing and implementing AI systems to transform raw data into actionable insights
Developing scalable machine learning models using Python and TensorFlow
Integrating data from various sources to create unified views for analysis
Building and maintaining data pipelines to support AI model deployment
Applying complex data analysis techniques to discern patterns and trends
Collaborating with team members to enhance AI solutions and drive business growth
Utilizing natural language processing tools like NLTK for text analytics and sentiment analysis
Implementing neural networks and deep learning methods for advanced AI applications
Managing data quality and infrastructure to support reliable AI operations
Engaging in continuous learning to adapt to new technologies and methodologies in AI engineering