Forward Deployed Engineer

Project Info:

The Forward Deployed Engineer builds and delivers production-grade, AI-powered systems directly with customers. This is a builder role: you will write, ship, and operate code in real customer environments. You bridge product intent, engineering execution, and real-world deployment, owning solutions end to end. From discovery through production, you make sure the agentic system delivers measurable customer and business impact.

Responsibilities:

Design and deploy production AI systems

• Design, build, and deploy production-ready AI systems in real customer environments.

• Implement agentic AI solutions: RAG and retrieval, orchestration, tool and function calling, multi-step workflows.

• Translate ambiguous customer problems into shippable technical architectures with clear trade-offs.

• Move solutions from proof of concept to production with measurable adoption.

• Engineer for enterprise-grade quality: secure, scalable, observable, resilient.

Prove the system works, and keep proving it

• Build the evaluation harness: baseline quality metrics, regression gates, safety checks, offline evals before anything ships.

• Establish deployment patterns, evaluation loops, and monitoring frameworks that survive after you leave.

• Implement reliability patterns: retries, fallbacks, idempotency, and safe failure modes for agentic workflows.

• Instrument for traces, dashboards, and alerts covering reliability, latency, and cost per outcome.

• Tune for performance and economics without trading away quality.

Build with AI coding agents, and be the editor of what they produce

• Command specialized AI coding agents (Cursor, GitHub Copilot, Devin, custom LLM scripts) as you would junior developers, then refine their output into robust, maintainable production code.

• Act as technical editor for AI-generated code: catch the specific failure modes that LLMs introduce before they reach production.

• These are two different jobs and you will do both. Evaluating the system you deliver to the customer is about accuracy, grounding, and safety. Reviewing code an agent wrote for you is about the mistakes that only LLM-generated code makes. Being good at one does not make you good at the other.

Own the security posture of what you ship

• OWASP fluency, secrets management, rigorous input validation, auth and authz correct by default.

• LLM-specific defenses: prompt injection, access controls, PII boundaries between the customer's data and the model.

• Security and privacy fundamentals applied in the implementation, not documented as an intention.

Own end-to-end customer delivery

• Lead delivery from discovery and architecture through rollout, iteration, and operational readiness.

• Make pragmatic architectural decisions under delivery pressure and inside someone else's stack.

• Build and maintain the integration plumbing: APIs, identity, data sources, legacy systems, observable data flow across the stack.

• Measure success by delivered impact and adoption, not by effort or billable time.

Communicate at customer and executive altitude

• Explain architecture, risks, and constraints to executives and non-technical stakeholders in decision-ready language.

• Frame decisions across scope, reliability, speed, and cost, and push back constructively when the ask and the constraint do not fit.

Transition delivery into durable ownership

• Hand over a system that operates without you: clear ownership, documentation, monitoring, support and iteration plans.

• Mentor engineers in production-grade AI delivery practices and reusable reference architectures.

• Capture reusable patterns as accelerators and playbooks so the next deployment starts

Requirements:

• Seniority: 7+ years in software engineering, architecture, or technical delivery, at Senior Developer or Technical Team Lead level, with a strong record of shipping production systems.

• Customer-embedded delivery: demonstrated discovery-to-deployment work in enterprise environments, in the customer's own setting.

• Backend and integration engineering: hands-on with APIs (REST/GraphQL), services, and data integrations. Strong backend orchestration in Python and Node.js. Strong debugging skills.

• LLM application engineering: experience building with LLM APIs (Anthropic/Claude preferred), tool calling, RAG, orchestration, and creating eval and quality gates.

• AI-first engineering mindset: proven experience integrating AI deeply into your daily workflow, and treating AI coding agents as junior developers whose work you review.

• AI-native code-review fluency: you know the common failure modes of LLM-generated code and can review, refactor, and harden it to enterprise standards.

• Production discipline: zero-downtime mindset, CI/CD, environments, containerization (Docker, Serverless), robust logging, database migrations, rollback procedures, incident response.

• Cloud: hands-on experience with at least one of AWS, Azure, or GCP.

• Security depth: OWASP fluency, secure handling of secrets and sensitive data, rigorous input validation, confident auth and authz implementation, PII handling.

• Communication: able to lead technical conversations with non-technical stakeholders and translate trade-offs for decision-makers.

Benefits:

General benefits - depends on the form of employment

• Hybrid work model combining office & remote work

• Attractively located office with collaboration spaces

• Onsite parking space for employees

• Referral program with financial bonus

• Life Insurance

• Budget for development (including language courses and others), clear career path with the possibility to gain experience in international environment

• Access to internal Learning Platform with multiple trainings oriented for professional growth

Lifestyle benefits:

• Access to MyBenefit platform (Multisport included)

• Team Building activities

• Charity initiatives

• Working environment promoting diversity and inclusion

Health benefits:

• Private medical care - Platinum Package