Forward Deployed Engineer

Role Type: Client-facing, hands-on engineering + solutioning (pre-sales through early delivery)
Location: Major US hub (e.g., New York, Bay Area, Dallas, Chicago – flexible for the right candidate). Ideally on site, then hybrid, then remote all options are possible but preferred in that order
Level: Manager/Principal/Senior Principal
Business Unit: Enterprise AI Platforms & Agentic Solutions
Reporting to: Artificial Intelligence Leader, North America

Company Overview:

Our client is IT Company and they specialized in enabling digital transformation for enterprises and technology providers by delivering seamless customer experiences, business efficiency and actionable insights.

Role summary

Our client is building and scaling enterprisegrade GenAI + Agentic AI capabilities (Foundry, orchestration, governance/AgentOps, accelerators, and verticalized agents) across Fortune 2000 clients.

As a Forward Deployed Engineer (FDE), you will operate at the intersection of executive problem framing, rapid engineering, and scalable architecture. You will engage directly with client CXOs and sssenior leaders to identify a highvalue wedge, build a working proof in weeks, and translate that into a clear path toward MVP and scaled rollout on client company or nay hyper-scaler platforms.

You will be embedded in deals and client journeys—shipping real agentic systems, not slides.

Key Responsibilities

1) Executive-facing discovery & value shaping

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).
Identify the right “thin-slice” wedge for a rapid Proof of Value (PoV): high signal, measurable, low dependency, enterprise-relevant.
Communicate trade-offs clearly: what to automate vs assist, where human-in-the-loop is required, and what “safe scale” entails.

2) Rapid Proof of Value engineering

Build a rapid PoV that demonstrates business impact + technical feasibility trustworthiness
Implement agent workflowswith tooluse, integration to enterprise systems, and lightweight UI/experience for stakeholder clarity.
Create initial evaluation harness (task success, accuracy, policy compliance, tool-call success, latency/cost) and capture baseline vs PoV lift.

3) Agentic blueprint & path-to-scale handoff

Produce a scalable Agentic Blueprint: agent roles, orchestration patterns, tools/APIs, data/knowledge strategy, guardrails, observability, and HIL escalation.
Define MVP backlog and architecture decisions: identity/access, audit, logging, fallback design, policy boundaries, and deployment model.
Hand off cleanly to delivery teams (MVP squads / pods) with a production-ready plan.

4) Solution architecture on client ecosystem

Design solutions using constructs or hyper scaler Agentic AI foundations (Foundry, knowledge fabric/RAG patterns, agent orchestration, governance, observability/AgentOps
Deploy across hyperscalers (AWS/Azure/GCP) and/or client environments; integrate with enterprise apps (e.g., ServiceNow, Salesforce, SAP, Microsoft stack).
Ensure alignment with enterprise security, privacy, and compliance requirements.

5) Deal acceleration & differentiation

Serve as a technical “closer” in competitive pursuits: demos, workshops, architecture deep dives, and technical validation.
Create reusable accelerators: prompt packs, workflow templates, connectors, eval suites, reference architectures—so future PoVs become faster and repeatable.
Contribute to BlueVerse thought leadership and field playbooks (industry use cases, POV patterns, autonomy ladder constructs, commercial hooks).

What success looks like (12-month outcomes)

Time-to-first-value: PoVs that reliably land in weeks with measurable outcomes.
Conversion: Strong PoV to MVP conversion rate (and clear roadmap to scale).
Executive confidence: CXO trust built through crisp narratives + working systems.
Reusable assets: Field-ready accelerators that reduce PoV cycle time quarter over quarter.
Scalability: PoVs designed with production realities in mind (security, observability, cost, controls).

Required Qualifications

10+ years of experience, recent hands-on full-stack .NET/C#/ASP.NET and Azure
Demonstrated ability to build quickly under ambiguity and still maintain sound architecture instincts.
Strong experience with modern AI stacks, including:
LLMs / agent patterns: orchestration, tool-calling, planning/execution loops, memory patterns
RAG & knowledge: embedding strategies, chunking, vector databases, retrieval evaluation
Evaluation: offline + online evals, regression suites, safety tests/red-teaming basics
Strong software engineering fundamentals: APIs, data pipelines, integration, distributed systems basics.
Proficiency in Python + one of (TypeScript/Java/Go); comfortable across frontend + backend as needed.
Cloud-native experience (AWS/Azure/GCP), containers, CI/CD, and observability fundamentals.
Exceptional communication: can translate technical complexity into business impact for senior stakeholders.

Preferred Qualifications

Proven delivery of GenAI/agentic solutions in production (not only prototypes).
Experience integrating with enterprise platforms: ServiceNow, Salesforce, SAP, Microsoft (Graph/O365), Databricks/Snowflake, etc.
Experience with security & governance for GenAI: data boundaries, access control, auditability, model risk, compliance evidence.
Familiarity with LLMOps/AgentOps concepts: versioning, telemetry, drift monitoring, cost governance (FinOps).
Domain depth in one or more LTM focus verticals (Travel/Transportation/Hospitality, Retail/CPG, BFSI, Manufacturing, Media).
Relevant certifications (cloud, security, data) are a plus.

Core competencies (what distinguishes top performers)

Executive presence: credible with CXOs; can run a room and drive decisions.
Structured problem framing: quickly identifies the highest-leverage wedge and measurement plan.
Builder mindset: ships working systems rapidly; demos are grounded in real integrations.
Judgment & trust: knows where agents must be constrained; designs for safe autonomy.
Repeatability: turns one-off wins into reusable playbooks and accelerators.

Working model

Travel: expected (client workshops, PoVs, exec meetings).
Operates in tight pods with Sales, Industry leaders, BlueVerse product/platform teams, and delivery squads.
Strong bias toward hands-on building (this role is not purely advisory).

Bonus

The range displayed on each job posting reflects the minimum and maximum salary target for the position across all US locations. Within the range, individual pay is determined by work location and job level and additional factors including job-related skills, experience, and relevant education or training.

Benefits

Comprehensive Medical Plan Covering Medical, Dental, Vision
Short Term and Long-Term Disability Coverage
401(k) Plan with Company match
Life Insurance
Vacation Time, Sick Leave, Paid Holidays
Paid Paternity and Maternity Leave

Forward Deployed Engineer .NET/Azure