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 matter expertise will 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, and maintain production-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, and optimize small, open, or purpose-built language models where they can achieve the right business outcome at lower cost than large frontier models.
- Build and operate multi-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 maximize retrieval quality 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 controls appropriate for internal 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 AI technologies so 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 or GraphQL backends, 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.
What You Need To Be Successful - 8+ years of experience building production software, ideally including ML systems and hands-on work with LLMs and Generative AI.
- Strong expertise in AI agent development, multi-agent systems, tool-using agents, and agent orchestration; foundation model development experience is helpful but not required.
- Programming: Python (FastAPI, NumPy, Pandas, scikit-learn, Pydantic, Jinja2) and Node.js; strong proficiency with APIs, distributed systems, and secure enterprise integrations.
- LLMs and frameworks: Hands-on experience with at least one major deep learning or LLM stack, such as PyTorch/Transformers or TensorFlow/Keras, and orchestration frameworks such as LangChain or LlamaIndex.
- 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, and optimizing small, 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 AWS Bedrock, OpenAI, Anthropic, Meta/Llama, and Mistral model ecosystems.
- Data and storage: SQL and NoSQL databases such as PostgreSQL and DynamoDB, Elasticsearch for search and analytics, and vector databases such as Pinecone, Weaviate, FAISS, Milvus, and pgvector.
- Cloud and infrastructure: AWS (S3, EC2, Lambda, CloudWatch, Fargate, EKS/ECS), Azure, GCP, Databricks, Docker, Kubernetes, Terraform, CI/CD, Airflow, and Kafka.
- Cost-efficient AI infrastructure: Experience deploying AI solutions on local, server-based, or cost-optimized environments and evaluating CPU, GPU, model architecture, and commodity hardware tradeoffs.
- Operational excellence: Load balancing, monitoring and alerting, debugging production issues, evaluation frameworks, latency optimization, throughput optimization, and cost/performance management.
- Governance and security: Strong understanding of secure AI deployments, contextual access controls, data governance, responsible AI usage, and enterprise risk considerations.
- Data platforms: Experience building or maintaining data lakes, warehouses, or lakehouse environments such as Snowflake, Delta Lake, BigQuery, MS Fabric, or Databricks.
- Finance partnership: Ability to partner with Finance subject matter experts and translate business problems into AI-enabled solutions. Finance industry or finance systems exposure is preferred but not required.
- Team enablement: Strong communication skills, product-oriented thinking, coaching ability, and the capacity to help business teams understand the art of the possible with AI.
- Education: BS, MS, or PhD in Computer Science, Electrical Engineering, Mathematics, or equivalent professional experience.
What Makes You Eligible - Ability to work from an office in Novi, MI, 3+ days per week (hybrid)
What We Offer - Access to employee discounts on world-class products (JBL, HARMAN Kardon, AKG, and more).
- Extensive training opportunities through HARMAN University.
- Competitive wellness benefits.
- "Be Brilliant" employee recognition and rewards program.
- An inclusive and diverse work environment that fosters and encourages professional and personal development.
#Hybrid
Salary Ranges:$ 125,250 - $ 183,700
