Where this role sitsThis is a hands-on backend engineering role that ships in the customer's world. You embed with a customer, understand their systems and data, and build the backend that connects Sapience AI to their reality.
You take MINERVA and the COGENT architecture from a general platform to a working deployment: the pipelines that bring a community's knowledge in, the services that connect to their CRM, AMS, and knowledge base, and the integrations that make collective intelligence real on their data.
You work in the field, close to the problem, with the speed that a customer deployment demands and the discipline that production and trust require.
Why this role existsEvery professional community stores its expertise differently. The value of collective intelligence depends on reaching that expertise where it actually lives, across systems that were never designed to connect.
General platforms do not deploy themselves. Someone has to build the backend that meets each customer where they are, fast, without cutting the corners that protect their data.
The Forward Deployed Backend AI Engineer is that builder. You turn a deployment from a plan into working software running against the customer's systems, and you feed what you learn back so the core platform gets better for everyone.
What you will own (Areas of Responsibility)You hold seven areas of responsibility inside the forward deployed team. Each one is yours to set direction on, build, and measure.
1. Deployment backend and integrations- Build the backend services and integrations that connect Sapience AI to a customer's CRM, AMS, knowledge base, and identity systems.
- Design data pipelines that bring a community's knowledge into the platform reliably and keep it current.
- Ship production-grade services under the time pressure of a live deployment, without sacrificing correctness.
2. Data ingestion and the knowledge layer- Build ingestion and transformation pipelines that feed the KO (knowledge object) graph and the retrieval layer COGENT relies on.
- Handle messy, fragmented, real-world data, including deduplication, entity resolution, and quality checks.
- Make the ingested knowledge searchable and trustworthy for members from day one.
3. MINERVA and COGENT integration- Integrate deployment-specific logic with the MINERVA platform and the COGENT architecture through their APIs and extension points.
- Adapt the platform's reasoning and retrieval to a customer's domain without forking the core.
- Contribute deployment patterns back into the platform so common needs stop being custom work.
4. Security, privacy, and data protection- Build every integration to keep community data protected, access controlled, and every action governed.
- Implement the customer's data-handling, residency, and access requirements correctly.
- Treat trust as a design constraint, not a review step at the end.
5. Reliability and operability- Instrument deployments so they can be monitored, debugged, and trusted in production.
- Build for failure: retries, backpressure, and clear error handling in the pipelines that feed the platform.
- Hand off running systems with the observability and documentation that let others operate them.
6. Field-to-platform feedback- Carry recurring deployment needs back to platform engineering so the core absorbs them over time.
- Turn one-off custom work into reusable connectors, templates, and libraries.
- Shorten the path from a new customer to a working deployment with every project.
7. Collaboration with the field team- Work alongside forward deployed sales and full stack engineers to deliver complete solutions.
- Partner with the customer's own engineers, and leave them able to run and extend what you build.
- Keep the field and the core platform teams in sync as the deployment evolves.
AI-augmented ways of workingYou build with AI as part of your toolchain. You use AI coding assistants and Sapience AI's own systems to move faster through integration work, generate scaffolding, and reason about unfamiliar systems, while you own architecture, correctness, and the choices that matter.
The standard is human in partnership: AI accelerates the work, you own the judgment, the interpretation, and the call. The people who create the most value here are not the ones producing the most output. They are the ones turning evidence into clear, durable decisions.
What this role is notTo keep the boundary clear:
- This is not a core platform-only role. You build in customer environments and carry patterns back to the core, rather than working solely on the central codebase.
- This is not a frontend role. You partner with full stack engineers on the interface, but your center of gravity is backend and data.
- This is not a research role. You apply and deploy AI in production, rather than developing new model architectures.
- This is not a hand-off-and-leave role. You are accountable for deployments that run reliably and stay protected after go-live.
What success looks likeWe measure this role on outcomes the team can see:
- Working deployments. Customers reach value on their own data because the backend you built runs reliably.
- Speed with safety. Deployments ship fast and still keep community data protected and governed.
- Reusable leverage. Custom work becomes connectors and templates that speed up the next deployment.
- Operable systems. What you build can be monitored, debugged, and run by others after handoff.
- Platform improvement. Recurring field needs make it into the core platform because you fed them back clearly.
- Data trust. Ingested knowledge is accurate, searchable, and trusted by members from the start.
Who you areRequired qualifications- Five or more years building backend systems in production, with strong software engineering fundamentals.
- Deep experience with data pipelines, APIs, and integrating disparate systems.
- Hands-on experience building with modern AI and ML systems, including LLMs and retrieval.
- Strong Python skills, plus fluency in at least one other backend language.
- Experience with distributed systems, databases, and cloud infrastructure.
- A track record of shipping reliable software under real deadlines.
- Care for security and data protection as part of how you build.
Preferred qualifications- Experience deploying software at customer sites in a forward deployed or professional-services-adjacent model.
- Familiarity with knowledge graphs, vector databases, and retrieval-augmented generation.
- Experience integrating CRM, AMS, or knowledge-platform systems.
- Comfort working directly with customer engineering and security teams.
- Exposure to neuro-symbolic or agentic AI systems.
How you work- You name the real problem in the customer's systems before reaching for a solution.
- You build fast and correctly, and you know which corners can never be cut.
- You treat data protection and trust as design constraints.
- You leave systems others can run, and documentation others can follow.
- You share reusable work so the whole team moves faster.
Skills & Competencies- Backend service design and API development for AI systems.
- Data ingestion, transformation, entity resolution, and pipeline engineering.
- Integration across enterprise systems and identity providers.
- Retrieval, embeddings, and grounding LLM behavior in trusted data.
- Observability, reliability engineering, and production debugging.
- Secure-by-design engineering for sensitive community data.
- Turning custom deployment work into reusable platform capability.
Services & Tools Experience- Python (primary), plus Go, Java, or TypeScript.
- Message queues, streaming, and workflow orchestration tools.
- Vector databases and graph databases (for example Neo4j-class systems).
- LLM orchestration and retrieval frameworks.
- Cloud platforms (AWS, GCP, or Azure), containers, and Kubernetes.
- SQL and NoSQL data stores, plus data-quality tooling.
- CRM, AMS, and knowledge-base APIs, and Sapience AI's MINERVA and COGENT interfaces (trained on the job).
Prior Experience & Background- Prior backend engineering at a software or AI company, ideally including customer-facing deployment work.
- Experience owning integrations against systems you did not control.
- A track record shipping production systems that handle sensitive data.
- Experience mentoring or setting patterns for other engineers is a plus.
Cross-functional partnersYou work most closely with the forward deployed sales and full stack engineers, Applied AI, Platform Engineering, and the customer's own engineering and security teams. You partner with core platform teams to feed recurring needs back into MINERVA and COGENT.
How we hireWe review every application, and we encourage you to apply even if you do not match every line above. Research shows that talented people, especially those from underrepresented communities, often hold back when they do not meet every qualification. If that is the only thing holding you back, apply anyway.
CompensationBase Salary: $156,000 - $168,000 + early equity
Generous health and wellness benefits
