Forward Deployed Engineer (FDE) - Agentic, Americas

Job Description

Forward Deployed Engineer (FDE) – Agentic, Americas_Remote (Hartford, CT,Atlanta, GA,Daytona, FL,Houston, TX)_Full-Time(FTE)_Direct Hire

\n

\n

Position Title: Forward Deployed Engineer (FDE) – Agentic, Americas

\n

Job Type: Full-Time

\n

Base Salary: $200,000 to $250,000 + Best-in-class benefits

\n

Location: Remote (Hartford, CT,Atlanta, GA,Daytona, FL,Houston, TX)

\n

Willing to relocate to one or more of the following locations?

\n

* Hartford, CT

\n

* Atlanta, GA

\n

* Daytona, FL

\n

* Houston, TX

\n

\n

Job Description:

\n

Key Responsibilities:

\n

\n

1) Executive-facing discovery & value shaping:

\n

\n

* Partner with Sales, Client Partners, and leaders to run CXO---level working sessions that translate business pain into an Outcome Thesis (KPI tree, baseline, uplift hypothesis, operating constraints).

\n

* Identify the right “thin-slice” wedge for a rapid Proof of Value (PoV): high signal, measurable, low dependency, enterprise-relevant.

\n

* Communicate trade-offs clearly: what to automate vs assist, where human-in-the-loop is required, and what “safe scale” entails.

\n

\n

2) Rapid Proof of Value engineering:

\n

\n

* Build a rapid PoV that demonstrates business impact + technical feasibility trustworthiness

\n

* Implement agent workflowswith tooluse, integration to enterprise systems, and lightweight UI/experience for stakeholder clarity.

\n

* Create initial evaluation harness (task success, accuracy, policy compliance, tool-call success, latency/cost) and capture baseline vs PoV lift.

\n

\n

3) Agentic blueprint & path-to-scale handoff:

\n

\n

* Produce a scalable Agentic Blueprint: agent roles, orchestration patterns, tools/APIs, data/knowledge strategy, guardrails, observability, and HIL escalation.

\n

* Define MVP backlog and architecture decisions: identity/access, audit, logging, fallback design, policy boundaries, and deployment model.

\n

* Hand off cleanly to delivery teams (MVP squads / pods) with a production-ready plan.

\n

\n

4) Solution architecture on client ecosystem:

\n

\n

* Design solutions using constructs or hyper scaler Agentic AI foundations (Foundry, knowledge fabric/RAG patterns, agent orchestration, governance, observability/AgentOps

\n

* Deploy across hyperscalers (AWS/Azure/GCP) and/or client environments; integrate with enterprise apps (e.g., ServiceNow, Salesforce, SAP, Microsoft stack).

\n

* Ensure alignment with enterprise security, privacy, and compliance requirements.

\n

\n

5) Deal acceleration & differentiation:

\n

\n

* Serve as a technical “closer” in competitive pursuits: demos, workshops, architecture deep dives, and technical validation.

\n

* Create reusable accelerators: prompt packs, workflow templates, connectors, eval suites, reference architectures—so future PoVs become faster and repeatable.

\n

* Contribute to the company thought leadership and field playbooks (industry use cases, POV patterns, autonomy ladder constructs, commercial hooks).

\n

\n

What success looks like (12-month outcomes):

\n

\n

* Time-to-first-value: PoVs that reliably land in weeks with measurable outcomes.

\n

* Conversion: Strong PoV to MVP conversion rate (and clear roadmap to scale).

\n

* Executive confidence: CXO trust built through crisp narratives + working systems.

\n

* Reusable assets: Field-ready accelerators that reduce PoV cycle time quarter over quarter.

\n

* Scalability: PoVs designed with production realities in mind (security, observability, cost, controls).

\n

\n

Required Qualifications:

\n

\n

* 8+ years of experience in full-stack engineering / solution architecture / technical consulting, with significant client-facing delivery.

\n

* Demonstrated ability to build quickly under ambiguity and still maintain sound architecture instincts.

\n

* Strong experience with modern AI stacks, including:

\n

* LLMs / agent patterns: orchestration, tool-calling, planning/execution loops, memory patterns

\n

* RAG & knowledge: embedding strategies, chunking, vector databases, retrieval evaluation

\n

* Evaluation: offline + online evals, regression suites, safety tests/red-teaming basics

\n

* Strong software engineering fundamentals: APIs, data pipelines, integration, distributed systems basics.

\n

* Proficiency in Python + one of (TypeScript/Java/Go) ; comfortable across frontend + backend as needed.

\n

* Cloud-native experience (AWS/Azure/GCP), containers, CI/CD, and observability fundamentals.

\n

* Exceptional communication: can translate technical complexity into business impact for senior stakeholders.

\n

\n

Preferred Qualifications:

\n

\n

* Proven delivery of GenAI/agentic solutions in production (not only prototypes).

\n

* Experience integrating with enterprise platforms: ServiceNow, Salesforce, SAP, Microsoft (Graph/O365), Databricks/Snowflake, etc.

\n

* Experience with security & governance for GenAI: data boundaries, access control, auditability, model risk, compliance evidence.

\n

* Familiarity with LLMOps/AgentOps concepts: versioning, telemetry, drift monitoring, cost governance (FinOps).

\n

* Domain depth in one or more LTM focus verticals (Travel/Transportation/Hospitality, Retail/CPG, BFSI, Manufacturing, Media).

\n

* Relevant certifications (cloud, security, data) are a plus.

\n

\n

Compensation:

\n

*Full-time

\n

*Benefits - Full

\n

*Relocation Assistance Available - Possible for ideal candidate

\n

\n

Candidate Details:

\n

*7+ to 10 years experience

\n

*Seniority Level - Director

\n

*Minimum Education - Bachelor's Degree

\n

*Willingness to Travel - Occasionally

\n

\n

Ideal Candidate:

\n

\n

Core competencies (what distinguishes top performers)

\n

\n

* Executive presence: credible with CXOs; can run a room and drive decisions.

\n

* Structured problem framing: quickly identifies the highest-leverage wedge and measurement plan.

\n

* Builder mindset: ships working systems rapidly; demos are grounded in real integrations.

\n

* Judgment & trust: knows where agents must be constrained; designs for safe autonomy.

\n

* Repeatability: turns one-off wins into reusable playbooks and accelerators.

\n