• Lead AI/LLM strategy, solution architecture, and implementation across Engineering, Operations, and Project Delivery. • Build and maintain LLM-based agents to support: intelligent processing of technical documentation, automated design validation and engineering workflows, testing and QA automation, knowledge retrieval and contextual reasoning. • Integrate AI into core power automation workflows: IEC 61850 SCD engineering files, relay settings, SCADA HMI & logic, substation documentation, etc. • Establish AI governance, secure data pipelines, and compliance with utility-grade cybersecurity standards. • Partner with engineering managers and subject-matter experts to identify high-value AI automation opportunities. • Develop scalable pipelines for inference, fine-tuning, continuous learning, and lifecycle management in cloud and on-prem environments. • Evaluate and incorporate emerging AI technologies (RAG, vector stores, autonomous agents, internal copilots). • Monitor model performance, accuracy, drift, and cost; lead improvement cycles and risk mitigation. • Train and coach engineering teams on practical AI tools and adoption in daily workflows. • Ensure compliance with GE Vernova global standards, regulatory expectations, and utility-sector requirements.

• Join an existing development team to build and ship LLM-powered features in a complex, large-scale production application. • This is a hands-on, full-stack role spanning backend services, APIs, and the LLM systems (retrieval, agents, and evaluation). • Leverage AI coding tools and autonomous agents to write code, automate workflows, and optimize delivery. • Work closely with globally distributed teams across multiple time zones, owning features end-to-end. • Bring senior-level expertise in Python and LLM engineering, plan, execute, and deliver technical solutions.

• Design, refine, and optimize prompts and prompt pipelines for LLM-based applications. • Build structured prompt frameworks (few-shot, chain-of-thought, role prompting, tool prompting). • Evaluate AI outputs for accuracy, bias, hallucination, and reliability. • Work with engineering teams to integrate prompts into APIs and AI products. • Develop prompt libraries and reusable templates for various use cases. • Run experiments to benchmark prompt strategies across different LLMs. • Create guardrails and evaluation methods for safe and consistent AI responses. • Collaborate with product, data science, and engineering teams to improve AI performance.

• Build and maintain agentic systems that continuously collect, process, and surface signals from social platforms, on-chain data, and ads ecosystems. Turn raw, multi-source data into structured intelligence that informs real account and campaign decisions. • Design and maintain the pipelines that power those systems: ingesting from multiple external APIs, enriching and modeling data, and building semantic layers that both humans and AI agents can actually use. • Build multi-step AI agents that automate research, synthesis, monitoring, and reporting. Not single-call LLM wrappers, but orchestrated systems with memory, defined tools, and structured output schemas that teams can depend on. • Build full-stack applications and dashboards that make intelligence accessible across client accounts. Live tools, not decks. • Spend real time with internal teams across marketing, PR, content, growth, and social before writing a line of code. Map how work actually moves, identify where signal gaps create friction, and build systems that solve for multiple teams from a single architecture. • Stay ahead of the AI tooling landscape. Know when a no-code connector is the right call and when you need to build. Define how impact gets measured, and build the reporting to prove it.