The Associate AI/ML Engineer, Data Engineering will support the design, development, and deployment of artificial intelligence (AI) and machine learning (ML) solutions that address business challenges and improve operational efficiency. This entry-level role is ideal for recent graduates who are eager to apply their academic knowledge and grow their technical skills in a collaborative environment. The ideal candidate will have foundational knowledge of AI/ML concepts, Python programming, and data engineering principles gained through coursework, internships, research, or personal projects. Exposure to machine learning frameworks, cloud platforms, and software development best practices is preferred. The candidate will work closely with data scientists, data engineers, and software developers to help build, test, and maintain scalable AI/ML systems while gaining hands-on experience with modern AI technologies, MLOps practices, and production-quality development standards. Duties and Responsibilities, aligned with Key Results: People • Use a variety of programming languages and tools to develop, test, and maintain data pipelines within the Platform Reference Architecture. • Working directly with management, product teams and practice personnel to understand their platform data requirements • Maintaining a positive work atmosphere by behaving and communicating in a manner that encourages productive interactions with customers, co-workers and supervisors • Developing and engaging with team members by creating a motivating work environment that recognizes, holds team members accountable, and rewards strong performance • Fostering an innovative, inclusive and diverse team environment, promoting positive team culture, encouraging collaboration and self-organization while delivering high quality solutions Client • Collaborating on an Agile team to design, develop, test, implement and support highly scalable data solutions • Collaborating with product teams and clients to deliver robust cloud-based data solutions that drive tax decisions and provide powerful experiences • Analyzing user feedback and activity and iterate to improve the services and user experience Value • AI/ML Model Development: Design, build, and optimize machine learning models and AI solutions using techniques such as supervised/unsupervised learning, deep learning, natural language processing (NLP), and computer vision. Use frameworks such as TensorFlow, PyTorch, Keras, XGBoost, Scikit-learn and MLFlow. NLP experience includes NLTK, BERT, GPT. Ensure the models can be deployed and scaled in cloud environments like Azure ML, Azure Document Intelligence Databricks, and/or AWS SageMaker. • Data Apps Development: Have solid understanding in software best practices for flexible, extensible microservice application architectures that reside in an overall dynamic distributed system. Understand how to implement resilient, robust production-grade code that runs in a cloud environment in container services like AKS, EKS, ECS, Container Apps in Azure and/or AWS and interfaces with other cloud and application services. • End-to-End AI/ML Pipeline: Develop and maintain scalable AI/ML pipelines, from data ingestion and preprocessing to model training, validation, and deployment using MLOps best practices. Have hands-on experience with technologies like MLFlow. • Generative AI Applications: Apply GenAI techniques to real-world business cases, developing models that generate data-driven insights, automate processes, and enhance operational efficiency. Apply agentic flows and leverage RAG solutions where appropriate. Have a firm understanding of similarity / RAG basic and advanced patterns, and agentic flows. Be hands-on with fine-tuning and libraries like Langchain, Ollama Llamaindex, Langroid, CrewAI, VanniAI. Understand and have experience with Vector DBs like Milvus, OpenSearch, Azure AI Search, and PGVector. • AI/ML Solution Deployment: Deploy machine learning and AI models in production environments using cloud platforms like AWS, Azure, using SageMaker, Azure Document Intelligence, Azure ML and/or Databricks ensuring robust integration with existing systems. Understand cost savings trade-offs for training and inference and leveraging serverless soutions. • AI Innovation & Application: Work on cutting-edge AI initiatives, including generative AI, reinforcement learning, and neural network architectures (e.g., CNNs, RNNs, Transformers), applying them to real-world use cases. • Model Monitoring & Optimization: Implement model monitoring, performance tracking, and continuous improvement strategies, ensuring that AI/ML models maintain accuracy, performance, and scalability over time. Understand how to handle model and data drift. • Collaboration with Cross-Functional Teams: Partner with data scientists, data engineers, software developers, and product managers to understand business objectives, define AI/ML solutions, and deliver impactful results. • AI/ML Infrastructure & Automation: Build infrastructure for deploying and managing machine learning models at scale, incorporating MLOps practices to automate deployment, monitoring, and retraining in Databricks using MLFlow. Education and Experience: • Bachelor's and/or master's degree in Computer Science, Data Science, Engineering, or a related field. • Recent graduate or candidate with internship, academic project, research, or hands-on experience developing data and software solutions. • Foundational understanding of ETL processes and data integration concepts through coursework, internships, or personal projects. • Exposure to cloud-based data services and platforms such as AWS or Azure is preferred. • Familiarity with programming languages such as Python, Scala, Java, .NET, or similar technologies through academic or project experience. • Basic understanding of Windows and Linux environments and willingness to learn in a mixed-platform setting. Computer Skills: To perform this job successfully, an individual must have intermediate knowledge of Microsoft Project, Word, Excel, Access, PowerPoint, Outlook, and Internet navigation and research Certificates and Licenses: Valid driver's license required. Supervisory Responsibilities: None Work Environment: • Standard indoor working environment. • Occasional long periods of sitting while working at computer. • Must be able to lift, carry, push or pull up to 30 lbs. • Position requires regular interaction with employees at all levels of the Firm and interface with external vendors as necessary. • Independent travel requirement: As Needed