Forward Deployed Engineer (AI/GenAI)

As a Forward Deployed Engineer, you will work directly with enterprise customers to design, build, deploy, and continuously improve AI-powered solutions that solve real business challenges.

This is an engineering role with end-to-end ownership. You'll partner with customers to understand their business processes, data, and technology landscape, identify where AI can create measurable value, architect production-ready solutions, and lead their implementation from discovery through deployment and continuous optimization.

You'll combine software engineering, AI engineering, solution architecture, and customer consulting in a single role.

Success is measured by production adoption, customer outcomes, and business impact—not by the number of completed tickets.

What You'll Do

Customer Engagement & Solution Delivery

• Own AI solution delivery from technical discovery through production deployment and continuous improvement.
• Analyze customer business processes, engineering workflows, enterprise systems, and data landscape to identify high-impact AI opportunities.
• Help customers improve both business operations and software delivery (SDLC) through AI adoption where appropriate.
• Translate business challenges into scalable, production-ready AI solution architectures.
• Prototype rapidly, validate with business users and technical stakeholders, and iterate based on measurable outcomes.
• Present architectural decisions, trade-offs, implementation strategies, and technical risks to both technical and business audiences.
• Capture implementation learnings and transform them into reusable delivery patterns, accelerators, and best practices.

AI Engineering & Architecture

• Design, build, or extend enterprise applications with AI capabilities using the technology stack that best fits each customer's environment.
• Develop production-grade AI solutions using LLMs, AI agents, Retrieval-Augmented Generation (RAG), and modern orchestration frameworks.
• Integrate AI capabilities into enterprise applications, APIs, databases, cloud platforms, identity providers, messaging systems, and existing technology ecosystems.
• Design reliable AI workflows including tool calling, planning, memory, context engineering, retries, fallback strategies, guardrails, and human-in-the-loop mechanisms.
• Build evaluation pipelines, automated regression testing, golden datasets, and AI observability to continuously measure solution quality.
• Design deployment architectures that prioritize scalability, reliability, maintainability, security, and cost efficiency.
• Troubleshoot production issues, optimize deployed systems, and continuously improve customer solutions.

What We're Looking For

Required

• Proven ability to work directly with enterprise customers, understand business challenges, and translate them into production-ready AI solutions.
• 5+ years of commercial experience delivering production software systems.
• Strong software engineering fundamentals and proficiency in one or more modern programming languages (such as Java, C#, Python, JavaScript/TypeScript, Go, Kotlin, Rust, Scala, or similar).
• Experience designing distributed, cloud-native, or enterprise applications.
• Hands-on experience with modern LLM platforms such as OpenAI, Anthropic, Google Gemini, Azure OpenAI, Amazon Bedrock, or similar.
• Strong understanding of:
  • Retrieval-Augmented Generation (RAG)
  • Vector databases
  • Embeddings
  • Hybrid search
  • Context engineering
  • Prompt engineering
• Experience building AI-powered applications using modern AI SDKs, orchestration frameworks, or agent platforms (e.g. LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, Spring AI, Microsoft AI Extensions, or similar).
• Experience deploying and operating production applications on AWS, Azure, GCP, or other enterprise platforms using modern DevOps and CI/CD practices.
• Solid understanding of enterprise architecture, distributed systems, APIs, integration patterns, networking, identity and access management (IAM), security principles, and modern software delivery practices.
• Ability to balance speed, quality, scalability, maintainability, and cost when making engineering decisions.
• Excellent communication skills and confidence working directly with technical and business stakeholders.
• Upper-Intermediate English or higher.

Nice to Have

• Experience with AI evaluation frameworks, LLM benchmarking, and quality assessment.
• Experience designing golden datasets, automated regression testing, and evaluation pipelines for AI applications.
• Experience with AI observability platforms such as LangSmith, Langfuse, Arize Phoenix, MLflow, or similar.
• Knowledge of AI governance, responsible AI, guardrails, hallucination mitigation, and prompt injection protection.
• Experience with Model Context Protocol (MCP), Agent2Agent (A2A), and emerging AI interoperability standards.
• Experience integrating AI into existing enterprise applications regardless of technology stack.
• Experience with enterprise AI platforms such as Azure AI Foundry, AWS Bedrock, Vertex AI, Databricks, or similar.
• Previous experience in Solution Architecture, Technical Consulting, Customer Engineering, Technical Pre-Sales, or Professional Services.
• Experience working in regulated industries such as healthcare, finance, telecommunications, manufacturing, energy, or the public sector.

Location: Remote (Ukraine / Europe)
Employment: Full-time
Travel: Up to 20–30% (depending on client engagements)