Forward Deployment Engineer

What matters most

• Turn customer ambiguity into architecture

  The role works directly with customer engineering and business teams to convert ambiguous requirements into practical technical solutions. Prepare to explain how you would scope a use case, identify constraints, and communicate architecture trade-offs.

• Take GenAI from POC to production

  Tenarai calls for designing, deploying, and scaling AI solutions in customer environments, including agentic applications using RAG, LLMs, tool calling, orchestration, and multi-step workflows. Focus preparation on the decisions involved in moving such a system beyond a proof of concept.

• Make deployments reliable and measurable

  The posting specifies monitoring, logging, error handling, retries, fallbacks, safe failure, and deployment and evaluation frameworks. Be ready to discuss how these elements support production operation rather than treating a working model response as sufficient.

• Deliver across teams and hand over operations

  The engineer collaborates with AI, platform, product, and research teams; explains risks and progress to varied stakeholders; and documents solutions and operational support plans for handover. Prepare to describe clear ownership and communication through rollout and adoption.

Likely challenges to think through

• Balancing workflow capability with control

  A likely challenge is deciding when multi-step agents and tool calls add enough value to justify more failure paths. Prepare to compare simpler and more agentic designs, including how errors would be detected and contained; this is a preparation hypothesis, not a reported Tenarai problem.

• Trading off quality, latency, scale, and cost

  Because the role explicitly optimizes these dimensions, production decisions may pull in different directions. Prepare to explain how you would evaluate alternatives and set monitoring signals before choosing an architecture; the specific constraints will depend on the customer.

• Maintaining momentum through customer rollout

  Discovery, implementation, adoption, and handover involve different stakeholders and priorities. A plausible difficulty is resolving unclear requirements while leaving customer or platform teams able to operate the solution. Prepare an approach to documenting decisions, risks, and operational responsibilities.

Topics to prepare

• Customer discovery and solution architecture

  Required: translate ambiguous business needs into practical technical architectures. Revision exercise: take a vague customer request for an AI assistant and write clarifying questions, a scoped use case, an architecture sketch, and the key risks and trade-offs.

• RAG and agentic workflow design

  Required: understand RAG, LLM orchestration, prompt engineering, tool/function calling, and multi-step workflows. Revision exercise: diagram a request from retrieval through model response and tool calls; explain when each step is needed and how failures are handled.

• Production system design

  Required: design scalable, reliable AI systems using APIs, microservices, cloud platforms, and production deployments. Revision exercise: compare two deployment architectures and explain their scaling limits, dependencies, and rollout risks.

• AI reliability and operations

  The role calls for logging, monitoring, error handling, retries, fallbacks, and safe failure. Revision exercise: create a failure-mode table for an LLM workflow, specifying what should be logged, retried, returned to the user, or escalated.

• Evaluation and optimization trade-offs

  The posting calls for evaluation and monitoring frameworks and optimization of latency, performance, scalability, and cost. Revision exercise: define quality and operational measures for a RAG application, then explain how you would assess a faster but lower-quality configuration.

Skills to revise

• Build a Python LLM application

  Required: strong Python and hands-on GenAI/LLM application development. Practice implementing a small multi-step application with structured inputs, prompt construction, error handling, and tests; explain what must change before production deployment.

• Implement retrieval and tool calls

  Required: RAG and tool/function calling in agentic applications. Practice a workflow that retrieves relevant content, passes it to an LLM, invokes a bounded tool when appropriate, and handles an unavailable tool or empty retrieval result.

• Expose and deploy an API

  Required: APIs, microservices, cloud platforms, and production deployments. Practice wrapping an AI workflow in a Python API, specifying request validation, timeouts, and deployment configuration. FastAPI is listed as good to have, not required.

• Instrument and evaluate a workflow

  The role calls for monitoring, logging, and AI evaluation. Practice adding request-level logs and latency and error measures, then run a small set of test cases and summarize quality failures alongside operational results.

• Present a rollout and handover

  Required work includes customer delivery, stakeholder communication, documentation, and operational handover. Practice presenting an architecture and its risks in both technical and business terms, then draft a brief rollout checklist and support handover.

• Map the role to your evidence

  The posting requires 5+ years in relevant engineering or consulting work, strong Python, deployed GenAI applications, and customer or cross-functional delivery. Prepare a short evidence sheet linking each requirement to a specific example you can discuss; leave gaps unclaimed.

• Prepare a POC-to-production walkthrough

  Choose a real AI or GenAI deployment and outline the business problem, architecture, your contribution, rollout, measurable results, and trade-offs. The role explicitly involves taking solutions from proof of concept to production in customer environments.

• Sketch an agentic application

  For a hypothetical customer problem, draw a RAG and LLM workflow with retrieval, tool or function calls, orchestration, and multi-step behavior. Explain where prompt design, access controls, and human review would fit; present this as a practice exercise, not a Tenarai project.

• Rehearse a production design review

  Prepare a one-page architecture covering Python services and APIs, deployment, logging, monitoring, retries, fallbacks, and safe failure. Be ready to explain choices affecting reliability, latency, scale, and cost, all of which the posting highlights.

• Prepare evaluation and operations artifacts

  Draft a sample evaluation plan with task-specific quality checks and monitoring signals, plus a brief handover outline for customer or platform teams. These are preparation artifacts for the posting’s evaluation, observability, documentation, and operational-support responsibilities.

• Practice customer-facing discovery

  Write five questions that turn an ambiguous business request into scope, success measures, constraints, and a practical architecture. Rehearse explaining the resulting risks and trade-offs to both engineering and business stakeholders.

• Bring targeted questions for Tenarai

  Ask how customer discovery and rollout are handled, what defines a production-ready AI deployment, who owns post-handover support, and how adoption and system performance are measured. These questions follow from the advertised responsibilities; the posting does not provide the answers.