Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global professional services organization is seeking an AI Engineer to join its Internal Firm Services practice. The role focuses on applying data, algorithms, and software engineering to develop and deploy scalable Artificial Intelligence and Machine Learning solutions. The organization provides an environment where professionals can work with emerging technologies while contributing to innovative AI solutions and developing their technical expertise.
The AI Engineer will be responsible for transforming raw data into actionable insights, designing AI systems, developing scalable machine learning solutions, and implementing software and platform systems that enable AI and ML models to operate effectively at scale.
As an Associate, the successful candidate will contribute to projects while developing technical skills and industry knowledge. The role involves collaborating with stakeholders, building meaningful professional relationships, taking ownership of deliverables, and continuously developing expertise in AI engineering and emerging technologies.
Responsibilities
- Design and implement AI systems that transform raw data into actionable insights.
- Develop scalable machine learning models using Python and TensorFlow.
- Integrate data from multiple sources to create unified views for analysis.
- Build and maintain data pipelines that support AI model development and deployment.
- Apply advanced data analysis techniques to identify patterns and trends.
- Collaborate with team members to enhance AI solutions and support business growth.
- Utilize natural language processing tools such as NLTK for text analytics and sentiment analysis.
- Implement neural networks and deep learning techniques for advanced AI applications.
- Manage data quality and supporting infrastructure to ensure reliable AI operations.
- Continuously develop technical knowledge and adapt to emerging AI technologies and methodologies.
- Build and orchestrate AI agent workflows using frameworks such as LangGraph to automate multi-step reasoning and integrate tools and APIs.
- Apply generative AI techniques, including prompt engineering, LLM evaluation, and fine-tuning, to develop production-ready applications powered by foundation models.
- Develop automated evaluation frameworks, including LLM-as-judge pipelines, regression testing, and adversarial benchmarking.
- Optimize open-weight language models such as LLaMA, Mistral, and Gemma for local and cloud deployment.
- Design agent harnesses and implement context engineering, memory management, retry logic, and structured output validation for reliable AI workflows.
Required Qualifications
- Bachelor’s degree or, in lieu of a degree, three years of specialized training and/or progressively responsible engineering experience involving AI and Machine Learning for each missing year of college.
- At least 1 year of relevant professional experience.
- Educational background in at least one of the following fields:
- Computer and Information Science
- Computer Engineering
- Computer Management
- Management Information Systems
- Information Technology
Preferred Qualifications
- Certifications aligned with data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials.
- Experience building and orchestrating AI agent workflows using frameworks such as LangGraph.
- Experience applying Generative AI techniques, including prompt engineering, LLM evaluation, and fine-tuning.
- Experience developing automated LLM evaluation frameworks, including LLM-as-judge pipelines, regression testing, and adversarial benchmarking.
- Experience optimizing open-weight language models, including LLaMA, Mistral, and Gemma.
- Knowledge of quantization, inference acceleration, and model-routing techniques.
- Experience designing agent harnesses and implementing context engineering, memory management, retry logic, and structured output validation.
- Demonstrated proficiency in Python and TensorFlow.
- Experience using machine learning libraries such as Scikit-Learn.
- Strong capabilities in complex data analysis and pattern recognition.
- Experience implementing AI solutions using open-source technologies.
- Experience applying natural language processing techniques to real-world applications.
Disclaimer
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