Type

Full-time

Experience

8+ yr

About this role

Role:  Forward  Deployed  Engineer  (FDE)   –  Agentic  AI  ,  Americas
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). Travel Expected.
Level:  Manager/Principal/Senior Principal
Business  Unit:   (Enterprise AI Platforms & Agentic Solutions).
Reporting  to:   Artificial Intelligence Leader, North America.

About Company:

Global technology consulting and digital solutions company that enables enterprises across industries to reimagine how they operate, compete, and grow. By combining deep domain knowledge, advanced engineering, and transformation expertise, delivers measurable business impact at scale.

Enterprise AI business unit, purpose built to help organizations move beyond pilots into scaled, product iongrade AI adoption, functional consulting, an AI Foundry, and agentic AI platforms to deliver integrated solutions that transform how work gets done across the enterprise.Operating across Banking, Insurance, Manufacturing, Retail/CPG, Energy & Utilities, Travel & Hospitality, HiTech, and Media,  works closely with industry GTM teams to create repeatable AI solutions, accelerators, and transformation programs aligned to real business outcomes.

Role  summary

Building and scaling enterprise grade  GenAI  +  Agentic  AI  capabilities (Foundry, orchestration, governance/AgentOp, 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 ssseni or 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  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  +  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  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

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

Benefits