Senior AI Engineer

Imagine what you could do here. At Brightpath Technologies, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job, and there's no telling what you could accomplish. Brightpath Technologies's Sales organization generates the revenue needed to fuel our ongoing development of products and services. This, in turn, enriches the lives of hundreds of millions of people around the world. We are, in many ways, the face of Brightpath Technologies to our largest customers. Brightpath Technologies's US Decision Intelligence (DI) team is looking for a talented individual who is passionate about crafting, implementing, and operating AI solutions that have a direct and measurable impact on Brightpath Technologies Sales and its customers. Description We're looking for a Senior AI Engineer with strong software development skills and a passion for applying LLMs and Agentic workflows to real-world business problems. You'll be responsible for building, testing, and optimizing intelligent agents, retrieval pipelines, and embedded AI features across our sales data platforms. Responsibilities: Design, prototype, and productionize LLM-powered applications that combine structured data, unstructured knowledge, semantic layers, and internal business logic Build agentic AI systems that can retrieve context, reason across data sources, call tools and APIs, generate insights, and support business decision-making. Partner with product, data science, design, engineering, and business stakeholders to translate ambiguous business problems into practical AI solutions Build modular APIs, SDKs, and micro-services to integrate LLMs, RAG pipelines, traditional ML models, data pipelines, and enterprise systems. Design secure and reliable integrations between LLMs, internal APIs, databases, knowledge sources, and enterprise tools. Partner closely with data science, engineering, and sales ops to embed context-aware intelligence in decision-making tools. Lead technical decision-making on infrastructure components, embedding safety mechanisms (e.g., autonomy sliders, grounding checks, model monitoring). Build scalable pipelines for multi-modal agent input, memory, and semantic routing. Collaborate closely with business teams to incorporate AI into their weekly cadences. Balance fast experimentation with production readiness, ensuring AI capabilities are scalable, measurable, reliable, and maintainable. Preferred Qualifications Hands-on experience building production-grade AI agents, including tool calling, routing, multi-step reasoning flows, agent handoffs, memory/session management, and human-in-the-loop patterns. Ability to balance rapid prototyping with production readiness, especially when moving from proof-of-concept to scalable enterprise features. Strong experience articulating and translating business questions into AI solutions. Communicate results and insights effectively to partners and senior leaders, as well as both technical and non-technical audiences. Sound communication skills - adept at messaging domain and technical content, at a level appropriate for the audience. Strong ability to gain trust with stakeholders and senior leadership. Familiarity with embeddings, retrieval algorithms, knowledge graphs, vector databases, hybrid retrieval, reranking, and graph-based approaches to enterprise knowledge modeling. Other complementary technologies for distributed systems architecture and asynchronous messaging, agent communication, and catching like RabbitMQ, Redis, and Valkey are preferred. Advanced Degree (MS or Ph.D.) in Economics, Electrical Engineering, Statistics, Data Science, or a similar quantitative field is preferred. Minimum Qualifications 10+ years of experience in ML, software engineering, or data science, with recent focus on Applied AI and LLMs. Ability to lead development of AI projects from start to finish. Proficiency in Python (FastAPI, LangChain, or similar frameworks), context engineering, and RESTful API design. Hands-on experience with LLM APIs, embeddings, vector databases, and RAG workflows. Solid grounding in data structures, async programming, and pipeline orchestration. Experience with agent orchestration frameworks such as LangGraph, Google ADK, CrewAI, AutoGen, or similar frameworks. Familiarity with Claude Code-style agentic engineering patterns, including subagents, hooks, MCP integrations, permissions, and session-based workflows. Bias for action, curiosity, and a collaborative mindset. Familiarity with telemetry and evaluation frameworks for AI agents. Ability to design business-context layers that combine structured data, semantic definitions, user permissions, domain logic, and unstructured knowledge to produce grounded AI responses. Strong time management skills with the ability to collaborate across multiple teams. Able to balance competing priorities, long-term projects, and ad hoc requirements. Ability to work in a fast-paced, dynamic, constantly evolving business environment. Comfortable with rap