Principal Engineer Finance AI Solutions

We’re a global, multi-disciplinary team that’s putting the innovative power of technology to work and transforming tomorrow. At HARMAN Automotive, we give you the keys to fast-track your career. Engineer audio systems and integrated technology platforms that augment the driving experience Combine ingenuity, in-depth research, and a spirit of collaboration with design and engineering excellence Advance in-vehicle infotainment, safety, efficiency, and enjoyment

About the Role

Drive hands-on delivery of AI and Generative AI solutions that help Finance teams streamline workflows, improve decision support, and deliver measurable business value. Success will be measured by hours saved, quality of user adoption, breadth of users served, responsible AI usage, and cost-efficient operation. This role is AI-first and finance-partnered: finance subject matterexpertisewill be provided by the Finance team, while the preferred candidate brings deep AI engineering capability and the ability to translate finance business problems into practical AI-enabled solutions.

You will architect, develop, andmaintainproduction-grade systems including AI agents, multi-agent workflows, agent-to-agent communication patterns, RAG pipelines, model routing, vector search, small or purpose-built language models, evaluation and guardrails, access controls, and observability. The role requires rapid prototyping, strong engineering discipline, and the ability to coach business teams as AI capabilities become embedded into day-to-day Finance work.

What You Will Do

Automate high-impact Finance workflows for internal stakeholders, prioritizing initiatives with the greatest time savings, business value, and user reach.

Partner with Finance subject matter experts to translate planning, reporting, analysis, controls, and operational challenges into AI-enabled solutions.

Design and develop AI agents and multi-agent systems that solve enterprise-scale Finance challenges, including agent-to-agent communication, tool use, orchestration, and contextual handoffs.

Deliver production-ready copilots and applications for knowledge search, document summarization, intelligent recommendations, conversational analytics, variance explanation, and end-to-end workflow automation.

Evaluate, select, tune, deploy, andoptimizesmall, open, or purpose-built language models where they can achieve the right business outcome atlowercost than large frontier models.

Build andoperatemulti-model AI ecosystems where different agents, tools, retrieval systems, and models interact safely and reliably.

Design and implement RAG pipelines over heterogeneous Finance and enterprise datasets, including policies, procedures, requirements documents, reports, business rules, lessons learned, and other unstructured content.

Select embedding strategies, chunking approaches, vector search configurations,rerankers, and routing policies to maximizeretrievalquality and business relevance.

Implement guardrails, content policies, safety filters, prompt and version management, latency and throughput tuning, cost controls, load balancing, fallback strategies, and model-routing patterns.

Define and implement governance frameworks for agent-based systems, secure information access, contextual access management, auditability, and responsible AI usage within Finance processes.

Deploy AI solutions on cloud, local, server-based, or cost-efficient environments, evaluating tradeoffs between CPU, GPU, model architecture, latency, throughput, and commodity hardware strategies.

Establish observability, evaluation frameworks, monitoring, model and data governance, and access controlsappropriate forinternal enterprise environments.

Rapidly prototype AI solutions alongside Finance and platform teams, then mature successful prototypes into maintainable production systems.

Teach, mentor, and coach Finance teams on AItechnologiesso AI capabilities are embedded within business teams rather than isolated in a separate engineering function.

Build internal applications using Python, Node.js, and modern web technologies with REST orGraphQLbackends, integrating securely with internal platforms and enterprise datasets.

Collaborate closely with requirements, testing, validation, security, governance, and platform teams; communicate proactively and iterate quickly in a fast-paced environment.

8+ years of experience building production software, ideally including ML systems and hands-on work with LLMs and Generative AI.

Strongexpertisein AI agent development, multi-agent systems, tool-using agents, and agent orchestration; foundation model development experience is helpful but notrequired.

Programming: Python (FastAPI, NumPy, Pandas, scikit-learn,Pydantic, Jinja2) and Node.js; strongproficiencywith APIs, distributed systems, and secure enterprise integrations.

LLMs and frameworks: Hands-on experience with at least one major deep learning or LLM stack, such asPyTorch/Transformers or TensorFlow/Keras, and orchestration frameworks such asLangChainorLlamaIndex.

Agent and protocol experience: Familiarity with MCP, agent-to-agent communication patterns, agent memory, tool calling, workflow orchestration, and evaluation of agent behavior.

Model strategy: Experience selecting, tuning, deploying, andoptimizingsmall, open, or purpose-built language models to achieve business outcomes while managing infrastructure and inference costs.

Model providers: Working familiarity connecting to inference providers such as… For full info follow application link.