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.
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
What You Must Have
- 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
What Sets You Apart
- 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
- Demonstrating 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
The salary range for this position is: $50,500 - $112,500. Actual compensation within the range will be dependent upon the individual's skills, experience, qualifications and location, and applicable employment laws. All hired individuals are eligible for an annual discretionary bonus. PwC offers a wide range of benefits, including medical, dental, vision, 401k, holiday pay, vacation, personal and family sick leave, and more. To view our benefits at a glance, please visit the following link: https://pwc.to/benefits-at-a-glance
As PwC is an equal opportunity employer, all qualified applicants will receive consideration for employment at PwC without regard to race; color; religion; national origin; sex (including pregnancy, sexual orientation, and gender identity); age; disability; genetic information (including family medical history); veteran, marital, or citizenship status; or, any other status protected by law.
PwC does not intend to hire experienced or entry level job seekers who will need, now or in the future, PwC sponsorship through the H-1B lottery, except as set forth within the following policy: https://pwc.to/H-1B-Lottery-Policy.
Learn more about how we work: https://pwc.to/how-we-work
For only those qualified applicants that are impacted by the Los Angeles County Fair Chance Ordinance for Employers, the Los Angeles' Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, San Diego County Fair Chance Ordinance, and the California Fair Chance Act, where applicable, arrest or conviction records will be considered for Employment in accordance with these laws. At PwC, we recognize that conviction records may have a direct, adverse, and negative relationship to responsibilities such as accessing sensitive company or customer information, handling proprietary assets, or collaborating closely with team members. We evaluate these factors thoughtfully to establish a secure and trusted workplace for all.
Applications will be accepted until the position is filled or the posting is removed, unless otherwise set forth on the following webpage. Please visit this link for information about anticipated application deadlines: https://pwc.to/us-application-deadlines
Skills Required
- Bachelor's degree (or equivalent combination of education/training and experience)
- At least 1 years of relevant experience
- Three years of specialized training/progressive work experience to substitute for missing college years (per job text)
- Proficiency with Python for AI/ML projects
- Experience with TensorFlow
- Familiarity with machine learning libraries such as Scikit-Learn
- Experience with NLP tools (e.g., NLTK) and applying NLP techniques in real-world applications
- Experience or certifications with cloud/data platforms (AWS, GCP, Azure, Databricks, Snowflake)
- Experience with LLMs, generative AI techniques, agent frameworks (e.g., LangGraph), or model optimization for deployment
What We Do
Build what’s next — with tech that matters PwC provides professional services across Audit and Assurance, Advisory and Tax — powered by a global network of over 370,000 people in 149 countries. You may know us for our business expertise, but technology is core to how we help clients move faster, build trust and deliver meaningful outcomes. As a technologist, you’ll work on agile teams with experienced engineers and product thinkers — using AI, cloud, cybersecurity and more to design scalable, real-world solutions. You’ll keep learning, stay challenged and be part of a network where your growth is built in — and your work drives what’s next.
Why Work With Us
At PwC, our professionals include technologists, engineers, programmers and consultants working across AI, cloud, cybersecurity, data and more. You’ll explore innovative ways to help clients transform their business through technology—reducing complexity, unlocking value and shaping a more agile, data-driven future.
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