Forward Deployed Engineer – Agentic AI

Responsibilities

• 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

• 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.
• 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.
• Experience with 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.