Forward Deployed Engineer – Agentic AI @ Tooploox

We are looking for a Forward Deployed Engineer to work directly with clients on high ambiguity GenAI, agentic AI and AI-enabled workflow transformation initiatives. This role is designed for someone who can operate at the intersection of customer discovery, hands-on engineering, solution architecture, early delivery and production adoption.

You will work closely with client stakeholders and users to uncover the real operational problem, build thin end-to-end solutions with real systems and data, validate feasibility, shape the delivery path and stay involved through MVP or first production release to assure alignment with expected outcome.

Role mission:

Your mission is to turn ambiguous customer problems into validated, production-grade AI solutions.

You will help move from “we can build this” to “we have proven this works in the customer’s real operating environment.” You will bridge presales, discovery, prototyping and delivery, while converting field learnings into solution blueprints and service accelerators.

• A seasoned hands-on technical leader who has evolved from full-stack or server-side software engineering into the GenAI and agentic AI domain.
• Experienced in designing, prototyping and integrating systems that combine LLMs, agentic frameworks, enterprise data and business workflows.
• Proficient at combining established engineering patterns with emerging AI capabilities to deliver reliable, production-ready solutions.
• Skilled in guiding teams and clients through ambiguity - able to prototype rapidly, validate assumptions and converge toward delivery-ready designs.
• Comfortable engaging with executives, product teams, operational users and engineers alike, translating complex AI concepts into clear, actionable solution narratives.
• Pragmatic, curious, collaborative and obsessed with customer problem - equally focused on technical soundness, business value, user adoption and delivery readiness.
• 8+ years of experience in IT, including strong experience in software design and engineering.
• 2+ years of hands-on experience architecting or building GenAI or agentic AI systems using modern LLM ecosystems, such as OpenAI, Anthropic, Gemini, Azure AI or AWS Bedrock.
• Experience supporting presales solutioning, discovery, prototyping or early delivery for AI engagements.
• Strong understanding of LLM orchestration, retrieval-augmented generation, vector databases, prompt engineering and tool-calling patterns.
• Understanding of cost modeling for AI workloads, including token usage, inference scaling and hosting models.
• Proficiency with at least one major cloud platform, such as AWS, Azure or GCP, and its AI/ML service offerings.
• Demonstrated experience leading technical discussions with both engineering and non-technical stakeholders in presales, discovery or early delivery phases.
• Strong command of software engineering fundamentals, API design, integration patterns and production-readiness practices.
• Knowledge of modern software delivery practices, including CI/CD, containerization, observability and DevSecOps.
• Excellent communication, presentation and documentation skills, with the ability to articulate complex solutions clearly and persuasively.

Optional:

• Prior experience as a Forward Deployed Engineer, Field Engineer, staff engineer, solution architect or technical product lead.
• Prior experience with traditional AI/ML systems, including model training, MLOps, data pipelines or feature stores.
• Familiarity with frameworks for agentic or multi-component AI pipelines.
• Exposure to Responsible AI, data privacy and governance frameworks.
• Hands-on experience with Databricks or Snowflake.
• Experience creating reusable solution blueprints, evaluation frameworks or technical accelerators.
• Industry-recognized cloud and AI certifications from Anthropic, AWS, Azure or Google.

We are looking for a Forward Deployed Engineer to work directly with clients on high ambiguity GenAI, agentic AI and AI-enabled workflow transformation initiatives. This role is designed for someone who can operate at the intersection of customer discovery, hands-on engineering, solution architecture, early delivery and production adoption.

You will work closely with client stakeholders and users to uncover the real operational problem, build thin end-to-end solutions with real systems and data, validate feasibility, shape the delivery path and stay involved through MVP or first production release to assure alignment with expected outcome.

Role mission:

Your mission is to turn ambiguous customer problems into validated, production-grade AI solutions.

You will help move from “we can build this” to “we have proven this works in the customer’s real operating environment.” You will bridge presales, discovery, prototyping and delivery, while converting field learnings into solution blueprints and service accelerators.

,[Lead discovery and solution shaping for GenAI / Agentic AI initiatives, working directly with client stakeholders, users and technical teams to understand workflows, systems, data and business constraints., Drive early-stage opportunities partnering with account executives, product analysts and technology SMEs to prototype, define scope, implementation roadmap and effort estimates., Translate ambiguous business problems into validated AI solution designs covering model strategy, data access, orchestration, tool use, integration patterns and production constraints., Build proof-of-concepts fast, prioritizing speed and impact over perfect design, technical spikes to validate architectural assumptions using real or representative systems and data., Evaluate and select appropriate AI frameworks, platforms and tools, including LLMs, vector databases, orchestration frameworks, cloud AI services and agentic tooling., Present, defend and explain complex AI, architecture and delivery concepts in simple, practical terms for both business and technical stakeholders., Stay involved through MVP or first production release to preserve context, support delivery teams and validate that the solution works in the customer’s operating environment., Define evaluation criteria for AI quality and business impact, including accuracy, groundedness, tool-call correctness, latency, cost, adoption and workflow effectiveness., Ensure proposed solutions adhere to security, compliance, Responsible AI, observability and production-readiness principles., Convert field learnings into reusable Solvd assets, including solution blueprints, evaluation frameworks and service accelerators.] Requirements: LLM orchestration, Cloud Platforms, GenAI, Agentic AI, AI Agents, LangChain, Solution Architecture, CI/CD, API Design, Customer Discovery, Client Engagement, MLOps, AI pipelines, Databricks, Snowflake