Hire a Forward Deployed Engineering Team That Answers to One Number You Already Track
Hire a forward deployed engineering team from Entrans when an AI pilot works in the demo but stalls before production. A senior engineer leads a small pod inside your systems, data, and release pipeline. The pod stays accountable to one measure you already track. Fees connect to the result delivered, not the hours logged.
Why Market Leaders Choose Entrans Forward Deployed Engineering Teams
One engineer can unblock an integration. Taking an AI system from pilot to production usually takes more: data work, agent builds, evaluation, and the controls your security team will ask for. A dedicated FDE team brings those skills as one unit, under one owner.
1. One Pod, One Business Measure
A senior engineer leads the pod and owns a metric your business already tracks, such as activation, retention, or AI-channel revenue. Every sprint is planned against that number.
2. Built Inside the Stack You Already Run
The team works in your cloud, your repositories, and your CI/CD. It connects to SAP, Salesforce, and your existing APIs instead of asking you to rip and replace them.
3. AI, Data, and Software Engineering in One Team
Agents fail when the data under them is not ready. Your FDE team covers data readiness, agent engineering, and the software around both, so work never stalls at a handoff between vendors.
4. Built for Production From the First Sprint
Integration, security, governance, and scale are planned from the start. Evaluation gates run before every release, so the system holds up after the demo is over.
5. Outcome Proven Before You Commit
We validate the outcome on your own data and your own problem before a formal engagement begins. You decide on evidence of a result, not on a proposal.
Hire FDE Teams From Entrans That Are Certified and Experienced
Entrans has 500+ technologists and holds ISO/IEC 27001:2022 and SOC 2 Type II certifications. Every forward deployed engineering team for hire is built from six capabilities, matched to where your AI work is stuck today.
Agent-Ready Modernization and Migration
The team modernizes legacy platforms and contact-center systems so agents can work inside them. Cloud and platform migration happens without a wholesale rip-and-replace, backed by our application modernization services.
AI Data and Knowledge Engineering
Engineers build RAG pipelines, vector storage, and knowledge graphs, then test data readiness against a target use case. Our data engineering and analytics practice supports the platform work, so every answer can cite your own data.
Agentic AI Engineering
The team builds custom MCP servers, multi-agent orchestration, and enterprise copilots. Work starts with one scoped agent on one workflow, drawing on our AI agent development practice.
AI Evaluation and Operations
Engineers set up LLM and agent evaluation, LLMOps and AgentOps, and AI red-teaming. Precision, recall, and F1 are gated before each release, so accuracy becomes a KPI you can track.
AI Governance, Trust, and Security
The team designs guardrails, audit trails, agent identity, and access and data boundaries. Controls map to your regulatory regime, with support from our cybersecurity and compliance team.
Model Engineering and Optimization
Engineers handle fine-tuning, distillation to small language models, and on-device or hybrid AI. You stop paying for more model than the outcome needs, and cost per outcome falls while performance holds.
Schedule Interviews With Your FDE Team and Onboard Within 48 to 72 Hours
FDE Development Team Technology Expertise
Your FDE development team works across the platforms Entrans partners with and the systems you already run. On the agent layer, the pod works alongside our agentic AI framework integration specialists.
Agent and AI Engineering
Evaluation, Governance, and Models
Cloud and Data Platforms
Enterprise Systems and Integration
Our Customer Success Stories
AI-Driven Invoice and GRN Reconciliation for an Enterprise Finance Team
Technical Stack: AWS Bedrock | LLM document extraction | Automated three-way matching | Exception review queue | Legacy ERP integration
Industry: Finance and Procurement
This finance and procurement team matched invoices, purchase orders, and goods receipt notes by hand. Documents came in as PDFs, scans, and structured records, and core systems had to stay on a legacy ERP. An Entrans FDE pod embedded with the finance operations and ERP teams and built against live documents inside the client's environment. An LLM layer on AWS Bedrock reads each format and matches every invoice to its PO and GRN. It flags quantity, price, and supplier mismatches before payment. With three-way reconciliation automated, manual effort fell 80 percent and payments now clear 2X faster.
AI Customer Service Agents in Production for a Global Retail Brand
Technical Stack: Agentic AI assistant | MCP tools | Secure APIs | Catalog, order management, and CRM integration | Policy guardrails | Human handoff
Industry: Retail and Consumer
A global retail brand selling online and in stores had chatbots that only handled scripted FAQs. Live orders, returns, and stock levels sat in separate ecommerce, order management, and CRM platforms. The Entrans FDE pod started by studying real customer conversations, APIs, and data flows, then scoped the workflows an agent could resolve end to end. The assistant now finds products, checks order status, and starts returns in a single conversation. Complex cases pass to service staff with full context. The retail AI agents went live on production catalog, order, and customer data, on a framework the brand can reuse for new workflows.
How to Hire a Forward Deployed Engineering Team With Enterprise Speed and Control
Building an FDE team in house means recruiting several hard-to-find roles at the same time. Our process runs in days, and you approve every engineer who joins the pod.
1. Share Your Requirements
Tell us the business problem, the systems involved, and the number that needs to move. One call usually scopes the pod and the right entry point.
2. Get Curated Profiles (Within 24 to 48 Hours)
You see the senior engineer who would lead the pod and the specialists around them. Each profile maps to the capability your use case needs first, such as data readiness or agent builds.
3. Evaluate and Interview
Interview each engineer on your terms. Where it fits, we validate the outcome on your own data before a formal engagement, so your decision rests on evidence.
4. Onboard and Kickoff (Within 48 to 72 Hours)
Access, security reviews, and contracts move in parallel. The pod joins your standups and starts work in your cloud and repositories.
5. Continuous Support and Scaling
Start with one workflow and expand as usage confirms value. The pod grows along one path: ground the data, build the agents, prove they work, govern them, then tune the economics.
Our Hiring Models
Dedicated FDE Team
A dedicated forward deployed engineering team embeds for the full program and answers to one business measure. Once the system is live, your team owns and runs it, or Entrans keeps it observed and governed through Managed AgentOps.
Team Augmentation
Add FDE capacity to the engineering team you already run. Your leads keep the roadmap and code review. If you need one specialist rather than a pod, hire a dedicated forward deployed engineer instead.
Project-Based Engagement
Begin with a fixed-scope AI Readiness Assessment or a Proof-of-Value Pilot. The pilot ships one scoped agent, copilot, or evaluation harness inside your workflow, with a measurable outcome attached.
Industries Where Our Forward Deployed Engineering Teams Deliver
Entrans serves enterprises across the US, UK, UAE, and India, and our FDE pods have taken AI to production in retail, healthcare, and finance. The work spans lending decisions, clinical information access, workflow automation, and software modernization.
Chief Executive Officer
Chief Executive Officer
Looking to Hire a Forward Deployed Engineering Team to Move AI From Pilot to Production?
Book a Free ConsultationFrequently Asked Questions
What is a forward deployed engineering team?
A forward deployed engineering team is a small pod of senior engineers who embed inside your company to build and ship one system in production. The pod works in your environment, with your data, against a measure your business already tracks. At Entrans, each pod covers AI, data, and software engineering, so one team can carry work from pilot to production.
Should I hire an FDE team or a single forward deployed engineer?
Hire one engineer when a single integration or deployment is blocked. Hire an FDE team when the work spans data readiness, agent builds, evaluation, and governance at once, since one person rarely covers all four. A team also keeps delivery moving when scope grows past the first workflow.
How much does it cost to hire a forward deployed engineering team?
Cost depends on the pod's makeup, the scope, and your entry point, from a fixed-scope assessment to a full build. Entrans links fees to the result delivered rather than hours logged. We also validate the outcome on your own data before a formal engagement, so you see evidence before you commit spend.
Who owns the system after the FDE team finishes?
You do. Under Build and Transfer, Entrans engineers the system inside your environment, and your team owns and runs it from day one of production. If you want ongoing help, Managed AgentOps covers observability, quality, and governance as a managed service.
How does a dedicated FDE team keep our data secure?
The team works inside your cloud and repositories, under your access rules. Entrans holds ISO/IEC 27001:2022 and SOC 2 Type II certifications. Guardrails, audit trails, and agent identity are built into the system. In regulated work such as healthcare, the team validates accuracy on de-identified documents before rollout.
