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Alinia AI enables safe deployment of AI in business-critical operations within regulated industries. As a Forward Deployed Engineer, you will deliver working solutions for regulated AI use cases, designing implementations around client problems, constraints, and existing architectures.
You will work hands-on with client systems, integrating Alinia's detection and safety capabilities alongside the client's models, API gateways, data pipelines, and approval processes. A significant portion of the role involves understanding complex client landscapes to make informed architectural decisions: what to integrate where, what to measure, and how to communicate findings—especially when results differ from expectations.
In large organizations, the person experiencing the problem, the team owning the system, and compliance stakeholders rarely align. You will listen to all perspectives, reconcile conflicting information, and form your own assessment of what is actually happening.
Key responsibilities:
- Work with Alinia's engineering and research teams to design and deliver client-specific integrations, prototyping when needed
- Serve as a bridge: translate client constraints into engineering requirements and explain platform capabilities/limitations back to clients
- Understand and validate client technical stacks
- Build reproductions, integrations, and evaluation harnesses in Python against Alinia's API and client systems
- Own technical delivery across multiple deployments from prototype to production
- Codify working patterns into reusable tools, playbooks, and building blocks
- Collaborate directly with client engineers, technical teams, and compliance experts
- Document findings for stakeholders who were not present in discussions
- Feed learnings back into product development; field gaps inform the roadmap
You will be the primary Alinia contact for clients during engagements, which typically run weeks to months. You will occasionally travel to customer sites.
Requirements (Non-negotiable):
- Ability to debug unfamiliar systems with incomplete information without guessing
- Measure before concluding; distinguish between findings and hypotheses
- Write production-grade Python; quickly grasp unfamiliar technical documentation, third-party APIs, cloud services, and external platforms
- Comfortable with clients: ask clarifying questions, respectfully disagree with senior stakeholders when evidence supports it, and hold unresolved problems openly rather than forcing premature closure
- Listen for the problem behind requests and validate your understanding before building
- Experience building or deploying LLM-powered or generative AI systems; understand how model behavior affects product experience
- Stay current with the field: new models, guardrail approaches, and tooling clients evaluate
- Understand where safety checks belong in agentic systems (tool calls, retrieval, reasoning, final answers)
- Fluent in English and Spanish (significant field work conducted in Spanish)
Desirable frameworks and technologies (not all required):
- Cloud platforms: GCP, AWS, Azure, and their managed AI services (Vertex AI, Bedrock, Azure OpenAI)
- API gateways and service meshes: Apigee, Kong, AWS API Gateway
- Infrastructure as code: Terraform
- Deployment and networking: containers, Kubernetes, VPC, private endpoints
- Identity and access: service accounts, OAuth, IAM roles
- Data and orchestration: RAG pipelines, vector stores, document extraction, orchestration frameworks
- Observability: Cloud Logging, Datadog, Grafana
- Agents and orchestration: LangGraph, CrewAI, MCP servers, Langfuse, LangSmith
- Background in security, DLP, compliance tooling, or ML evaluation is a plus
- Prior experience as a customer for similar products is valuable
Seniority is open; the company prioritizes fit over level.
