• Architect, build, and maintain AI‑first web and mobile applications using modern frameworks and engineering best practices • Lead the development of AI‑powered features using Python, LangGraph, and modern LLM tooling • Design and implement agentic workflow applications, including orchestration, tool use, stateful flows, and human‑in‑the‑loop patterns • Develop end‑to‑end solutions across the front end, back end, APIs, and cloud infrastructure • Write clean, scalable, and maintainable code in TypeScript (Node), JavaScript (React), and Python using established design patterns • Optimize applications for speed, scalability, reliability, and long‑term maintainability • Work closely with cross‑functional teams to define, design, and ship new features with strong ownership of outcomes • Provide architectural guidance, code reviews, and mentorship to engineering teams • Partner with clients to lead demos, support rollout discussions, and ensure successful adoption of AI‑powered solutions.

• Responsible for designing, developing, implementing, troubleshooting, and optimizing scalable, high-performance software and product applications • Assesses and defines software and product requirements, establishing the specifications and standards that guide scalable, high-quality development • Executes coding, debugging, testing, and troubleshooting across the full development lifecycle, incorporating AI-assisted and agentic development approaches to maintain quality and delivery efficiency • Develops and advances software and product capabilities that integrate with design systems, infrastructure, databases, and cloud-based platforms, all with the goal of maximizing operational efficiency • Evaluates application requirements and architect’s database solutions that ensure scalability, performance, and data integrity • Serves as a subject matter expert in AI-assisted development practices; acts as a resource for the engineering team on the effective and disciplined application of AI coding tools, informs development standards around AI use, and reinforces quality expectations through code reviews

• Design and build AI-powered applications and integrations across a variety of use cases • Lead implementation of small- to medium-scale AI solutions, with contribution to larger enterprise initiatives • Translate business requirements into scalable, supportable technical architectures • Design and implement data pipelines for AI/ML use cases (ingestion, transformation, and delivery) • Prepare and manage datasets for AI consumption, including cleansing, normalization, and feature engineering • Ensure data quality, lineage, and readiness for model training and inference • Apply best practices for handling sensitive data, including PII and regulated data • Evaluate and apply appropriate AI/ML (regression models, NLP, LLMs, Multi-modal models, embedding models, etc.) approaches based on use case. • Design and implement retrieval systems, including Retrieval-Augmented Generation (RAG) • Apply agentic AI patterns and architectures to enable multi-step reasoning and orchestration • Support training, fine-tuning, and optimization of models where applicable • Implement techniques such as model distillation, quantization, and performance tuning • Balance cost, performance, and accuracy in model selection and deployment • Build and integrate AI capabilities into enterprise systems, APIs, and workflows • Design scalable, secure architectures for AI services and applications • Ensure solutions are production-ready, maintainable, and aligned to enterprise standards • Engage with business stakeholders to gather and refine requirements • Clearly articulate technical concepts, trade-offs, and solution approaches • Validate requirements and translate them into actionable technical plans • Act as a bridge between business and engineering teams

• Develop practical AI, machine learning and optimization solutions that help technical and operational teams make complex industrial data easier to understand, explore, and act on. • Apply frontier AI models, including large language models, foundation models, and multimodal approaches, to create reliable tools for search, analysis, knowledge discovery, and decision support. • Build AI-enabled applications that combine models, data pipelines, prompts, agents, evaluations, optimization logic, and user feedback into solutions that are useful, explainable, and maintainable. • Design and use knowledge graphs, ontologies, and structured domain knowledge to connect process data, experimental data, documents, terminology, assets, materials, and business context. • Work with domain experts to translate scientific, engineering, process control, and operational questions into data products, model workflows, optimization approaches, and user-friendly analytics experiences. • Develop robust pipelines for structured and unstructured data. • Contribute to model validation, monitoring, documentation, governance, and continuous improvement so that deployed solutions can be trusted in real industrial environments. • Collaborate across data science, engineering, digital technology, operations, and business teams to deliver solutions that create measurable value and are designed for adoption.