AI Engineer

Job Description:

The Role

Dow Jones is seeking an AI Engineer/Solutions Specialist to join our Automation & Reporting team. You will design, construct, integrate and maintain AI Solutions into the existing and new workflows to optimize processes across our B2B Risk and Research teams. As a key team member, you will be involved in every aspect of the data science project development process, from data analysis and model selection to building proofs of concept and refining pipelines. You will leverage your expertise in statistical analysis and machine learning techniques to derive insights from data, address the organization's needs, and deliver tangible value through actionable outcomes. You will report to the Director, Automation & Reporting.

• Solution Design & Development: Own the development, deployment, and maintenance of machine learning models and LLM based solutions that automate business workflows and enhance reporting capabilities. Apply NLP and information retrieval techniques to extract structured information from unstructured text content.
• Data Engineering & Pipeline Management: Build and maintain robust data pipelines that support high-volume data processing, enrichment, and model training utilizing Python, SQL, and cloud services. Analyze, clean, and preprocess large datasets to optimize reusable ML models.
• Stakeholder & Portfolio Management: Collaborate cross-functionally with business, product, and engineering teams. Elicit requirements from stakeholders, define business problems, and translate them into well-scoped, technical solutions.
• Cloud & AI Integration: Leverage external APIs (including LLM APIs) for advanced data enrichment, preprocessing, or model integration. Deploy models and automation tools seamlessly using cloud-based services (AWS and GCP).
• Best Practices & Quality Assurance: Ensure high standards across solution design and development. Document workflows, validate results, and strictly follow industry best practices for model performance, versioning, and reproducibility.

You Have:

• Essential Experience: Bachelor’s or Master’s degree in a quantitative field (Computer Science, Data Science, Engineering, or a related STEM field). 3+ years of experience in a data science or applied ML role with a strong dual focus on both modeling and pipeline development.
• Technical Proficiency: Exceptional programming skills in Python and hands-on experience with standard machine learning libraries (e.g., scikit-learn, XGBoost, TensorFlow, or PyTorch), alongside modern AI frameworks and SDKs (such as LangChain or LlamaIndex). Proven experience designing, building, and deploying AI agents to automate complex workflows. Experience writing and optimizing complex SQL queries and handling large datasets. Familiarity with version control (Git) and reproducible workflows (e.g., notebooks, scripts, containers). 
• Cloud & API Expertise: Hands-on experience deploying and managing workloads within cloud infrastructure (preferably AWS, including tools like S3, Glue, Lambda, and Athena). Proven experience securely working with APIs, including integrating LLM APIs or external data sources.
• Delivery & Communication: Strong problem-solving skills and strict attention to detail. Exceptional communication skills, with a demonstrated ability to translate technical methodologies and results clearly to non-technical audiences and cross-functional teams.
• Preferred Skills: Experience operating within both AWS and GCP environments. Proven track record of applying advanced NLP methodologies to real-world business challenges. Familiarity with managing agile ML lifecycles from initial data exploration to production deployment.