Senior Forward Deployed Engineer — Finance AI Enablement

Omada Health

9h•$136,400 - $178,300•Remote

About The Position

Omada has a dedicated AI Transformation & Enablement organization focused on embedding AI directly into how business functions operate. Rather than acting as a centralized innovation lab disconnected from day-to-day operations, this team deploys senior engineers directly into functions to partner with teams, understand their workflows, and build production AI systems that drive measurable business impact. Finance is the next major deployment. As the Sr. Forward Deployed Engineer supporting Finance, you will operate as the embedded AI engineering partner for the Finance organization — attending planning reviews, close cycles, forecasting discussions, operational reviews, and executive preparation processes to deeply understand how Finance operates today and how AI can transform it tomorrow. This is a 0→1 build with a team behind you, focused on turning ambiguous, high-value finance problems into production systems used by executives and operators every day. You will own each agent end-to-end: from discovery and prototyping, to shipping, monitoring, and iterating. You will partner closely with the Sr. Director, Strategic Finance, who owns the Finance AI roadmap and business prioritization, while remaining part of Omada’s AI Transformation & Enablement engineering organization, where you will receive technical leadership, architecture guidance, peer collaboration, and engineering support from other Forward Deployed Engineers solving similar challenges across the company. This role is highly cross-functional and combines elements of AI engineering, systems integration, workflow automation, product thinking, business partnership, and organizational enablement. You will design, build, deploy, monitor, and continuously improve AI-powered systems that help Finance teams operate more efficiently, improve accuracy and consistency, accelerate decision-making, and scale institutional knowledge. The systems you build will operate within a highly regulated and compliance-sensitive environment that includes SOX, HIPAA, security, and data governance considerations. As part of the AI Transformation & Enablement organization, you will help shape the standards, governance patterns, and operational practices that define responsible enterprise AI adoption at Omada.

Requirements

5+ years of experience building and deploying software, data, automation, or AI-powered applications
2+ years of recent hands-on experience building LLM-based or AI-enabled systems in production environments
Strong experience designing AI workflows including retrieval systems, orchestration patterns, tool usage, evaluation frameworks, and multi-step reasoning systems
Strong proficiency in Python and experience building production-quality backend services, APIs, integrations, and automation workflows
Experience integrating enterprise systems and operational data into AI-enabled workflows
Experience working with enterprise business systems such as ERP, planning, financial, or operational platforms
Experience designing retrieval or contextual knowledge systems across large document and metric corpora
Familiarity with structured and unstructured enterprise data environments
Understanding of operational monitoring, evaluation, and observability concepts for AI systems
Excellent communication and stakeholder management skills
Proven ability to partner directly with non-technical teams and translate business workflows into scalable technical solutions
Experience enabling business users through training, documentation, and operational coaching
Comfortable operating within highly cross-functional and rapidly evolving environments
Thrives in ambiguity and 0→1 environments
Strong ownership mentality with the ability to independently drive initiatives forward
Comfortable balancing embedded business partnership with centralized engineering alignment
Demonstrates strong prioritization and judgment across competing initiatives
Awareness of governance, security, and compliance considerations related to enterprise AI adoption
Familiarity working within regulated or compliance-sensitive environments involving financial, operational, or healthcare-related data
Comfortable incorporating human review, evaluation, and auditability into AI workflows

Nice To Haves

Experience applying AI to Finance, Accounting, FP&A, Procurement, Revenue Operations, or operational business workflows
Experience working with platforms such as NetSuite, Adaptive Planning, FloQast, Workday, or enterprise data warehouse environments
Experience with AI orchestration frameworks such as LangChain, LangSmith, or similar tooling
Experience with AWS services, including Bedrock and modern cloud-native architectures
Experience extracting structured insights from unstructured enterprise documents using AI techniques
Prior experience operating as an embedded, Forward Deployed, Solutions, or customer-facing engineer
Strong perspective on responsible enterprise AI adoption and organizational AI enablement

Responsibilities

Design and build the Finance-specific AI integration layer that connects AI systems to enterprise finance platforms, operational data, and institutional knowledge sources.
Integrate AI systems with platforms such as NetSuite, Adaptive Planning, FloQast, AWS, and internal data warehouses.
Build retrieval and knowledge systems over financial documentation, board materials, KPI definitions, investor communications, and forecasting models.
Develop reusable AI workflow and orchestration patterns for Finance use cases.
Enable conversational and natural-language interaction with operational and financial data.
Partner with central Engineering and AI platform teams to adopt and extend shared AI infrastructure patterns.
Design systems that allow Finance teams to progressively own configuration, evaluation, and operational management of AI workflows over time.
Build AI-enabled quality assurance and validation systems that improve confidence, consistency, and operational rigor across Finance workflows.
Establish evaluation frameworks and review processes that ensure AI systems are reliable, measurable, and operationally trustworthy.
Develop AI systems that continuously analyze operational and financial processes to surface insights, anomalies, optimization opportunities, and business risks.
Focus on delivering practical AI solutions that improve operational efficiency and support faster, more informed decision-making.
Build AI-powered intelligence capabilities that help Finance leadership monitor external market activity and prepare executive-level materials more efficiently.
Develop AI-enabled workflows that accelerate operational execution and reduce manual effort across recurring Finance processes.
Partner closely with Finance SMEs to ensure workflows remain operationally accurate, transparent, and trusted.
Enable Finance teams to effectively evaluate, use, and extend AI systems over time.
Build evaluation frameworks, regression testing, and quality scorecards.
Establish operational review and feedback loops.
Train Finance SMEs on AI evaluation and workflow management.
Promote transparency around AI capabilities, limitations, and reliability.
Help Finance teams grow long-term AI fluency and operational ownership.