AI tools do not guarantee speed. METR 2025 found experienced developers were 19% slower with AI assistance, so access alone can slow delivery.
Forward Deployed Engineers
Andersen embeds forward deployed engineers inside your teams to move enterprise AI from pilot into production environments. These specialists combine solution architecture, hands-on development, and stakeholder collaboration, so AI initiatives reach real-world impact with lower delivery risk. Each engineer stays accountable from problem framing through stabilization and handoff.
The gap between using AI and delivering measurable engineering value
AI adoption can reduce output without delivery discipline. DORA 2024 found a 1.5% drop in throughput, showing that production engineering is needed to capture value.
AI-generated code is not production-safe by default. Veracode 2025 found 45% of samples contained flaws, raising risk, rework, and remediation cost.
How forward deployed engineering turns AI investment into value
A forward deployed engineer converts stalled AI pilots into working systems your teams actually use. Each outcome below pairs a business goal with the mechanism our engineers apply.
Move AI from pilot to production
Most AI proofs of concept never reach users. A forward deployed engineer takes ownership of implementation, wiring models into your infrastructure and APIs so a validated pilot ships to production in weeks, not quarters. You keep the momentum from the pilot instead of paying for a rebuild.
Cut the cost of failed and stalled initiatives
Abandoned AI projects burn budget without return. Our forward deployed engineers de-risk delivery through rapid prototyping and iterative delivery, so weak ideas are stopped early and funded work reaches production and measurable time-to-value. Kill decisions happen in weeks, not after a year of spend.
Increase adoption of your existing AI systems
Deployed models fail when nobody trusts or uses them. The engineer works directly with users on customer adoption, onboarding, and feedback loops, lifting active usage of systems you have already paid for. Small changes to workflows and prompts often unlock the biggest gains.
Close the gap between business goals and technical delivery
Requirements get lost between stakeholders and code. Our engineers handle requirements translation and technical architecture in one seat, turning business outcomes into shipped features without a translation layer that slows decisions.
Keep delivery stable as AI systems scale
Early wins collapse under production load. The engineer builds for reliability and performance, standing up scalable systems and real-time monitoring so quality holds as usage grows.
Build internal capability, not permanent dependency
Outside help should leave your team stronger. Every forward deployed engineer pairs delivery with documentation and knowledge transfer, so your staff can operate and extend the system after the engagement ends.
What our forward deployed engineers deliver
Andersen assembles forward deployed engineers with senior AI, ML, and data skills who own delivery inside your environment. Each capability below maps a service to the teams it serves and the outcome it produces.
A forward deployed engineer runs discovery with your stakeholders, frames the scope, and produces an AI solution architecture that ties technical architecture to a clear business case before any code is written, so investment decisions rest on evidence.
Engagements typically include:
- Solution design with build-versus-buy trade-offs;
- AI solution architecture and feasibility scoring;
- Integration mapping across your systems and data;
- A costed delivery roadmap aligned to business outcomes.
Our forward deployed engineers write production code alongside your team, applying full-stack development and generative AI engineering to ship features through customer-facing engineering rather than throwaway demos.
What this covers:
- Generative AI engineering with LLMs and retrieval;
- Full-stack development across web, service, and API layers;
- Hands-on development inside your existing repositories;
- Enterprise integrations and third-party APIs.
A forward deployed engineer handles deployment and production engineering for models and autonomous agents, standing up LLMOps and MLOps pipelines so agentic AI releases stay repeatable across production environments.
Delivery includes:
- LLMOps and MLOps pipelines with automated evaluation;
- Embedded execution inside your CI/CD and cloud platforms;
- Agentic AI workflows with guardrails and human review;
- Zero-downtime rollout across complex environments.
Our forward deployed engineers apply DataOps and data engineering to build the data pipelines that AI depends on, including RAG pipelines and semantic search over your own content, connecting sources into analytics-ready platforms with measured quality.
Scope covers:
- Data engineering and DataOps for batch and real-time flows;
- RAG pipelines and semantic search over enterprise content;
- Data quality engineering with validation and lineage;
- Enterprise integrations across warehouses and lakes.
The engineer keeps LLM models healthy after launch, tuning accuracy, latency, and cost so results stay within target as data drifts and traffic patterns change.
Work includes:
- Model monitoring with drift and accuracy alerts;
- Query and inference optimization for latency;
- Cost efficiency tuning for training and serving;
- Reliability and performance SLAs in production.
Our forward deployed engineers build AI security and compliance into delivery, adding automated testing and human review so AI systems meet enterprise controls before they reach users. Nothing reaches production without passing the same gates your platform team enforces.
Controls include:
- Secure coding and dependency scanning in the pipeline;
- Human review of AI-generated code before merge;
- Scalable systems designed for load and failover;
- Audit-ready logging and access control.
A forward deployed engineer closes the loop on customer success, lifting AI adoption and retention while documentation and knowledge transfer leave your team able to run the system alone long after the engagement ends.
Outcomes include:
- Onboarding and enablement for internal users;
- Feedback loops that shape the next iteration;
- Runbooks and handover documentation;
- Measured time-to-value and adoption metrics.
Structured delivery process
Each engagement with a forward deployed engineer moves through five stages, with defined client input and Andersen deliverables at every gate.
You share business goals, systems, and constraints. Andersen runs discovery, frames the problem, and delivers a readiness assessment covering scope, timeline, and a delivery risk register within two weeks. Nothing is committed until both sides agree on the plan.
Meet our forward deployed engineers
Marek S.
Forward Deployed Engineer
Python
AgentCore ecosystem
Strands SDK
LangGraph
MCP
RAG
AWS CloudWatch
Ivo R.
Forward Deployed Engineer
Python
Strands SDK
FastAPI
Pydantic
asyncio
LangChain
LangGraph
LlamaIndex
Alex V.
Senior Forward Deployed Engineer
Python
C++
JavaScript
TensorFlow
Apache Airflow
AWS
Docker
DeepEval
Why choose Andersen's forward deployed engineers
Every reason below carries a proof point, from certifications to named delivery metrics, so a decision to hire forward deployed engineers rests on evidence, not positioning.
Senior AI and data engineers
Andersen fields senior engineers with years of AI, ML, and data delivery behind them, not juniors learning on your budget, so a forward deployed engineer contributes from week one, as shown in this AI forecasting case study.
Governed delivery with human review
Every AI change passes human review before release, the control Veracode's 45% vulnerability finding shows is missing elsewhere, which keeps delivery risk and rework low. 100% of AI-generated commits require reviewer sign-off before merge, with zero exceptions in production repositories.
Outcomes over activity
Engagements are measured on shipped systems and time-to-value, with milestone acceptance instead of billable hours, so budgets track real progress and stop when the value is delivered. Acceptance criteria are documented and auditable at every milestone.
Certifications, partnerships and recognitions
Andersen backs its engineers with audited controls, quality management, and cloud credentials, giving clients independent proof of the standards behind embedded delivery.
Scale your AI delivery with embedded senior engineers
Click through to see the profiles, tech stacks, and delivery track record of the AI developers you can add to your team, so you can match seniority and skills to your project before you talk to us.
What our clients say
Clients describe how a forward deployed engineer moved AI work into production and lifted adoption across complex environments.
FAQ
A forward deployed engineer (FDE) is a senior specialist who joins your team to design, build, and ship AI systems inside your own environment. Unlike a remote contractor who only hands over code, an FDE owns delivery from problem framing to production, staying accountable for the result rather than a set of tickets.
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What happens next?
An expert contacts you after having analyzed your requirements;
If needed, we sign an NDA to ensure the highest privacy level;
We submit a comprehensive project proposal with estimates, timelines, etc.
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