Forward Deployed AI Engineering
An AI engineer embedded in your team, shipping AI inside your real systems.
- Ideal for
- Organisations with real data and systems that want AI shipped inside them
- Format
- Embedded with your team, on-site in Dhaka or remote
- You get
- Production AI your people use, plus the knowledge to own it
The short version
An AI engineer embedded with your team, working inside your systems, data and constraints to take AI from idea to a production deployment your people actually use.
Best for organisations that want AI shipped inside their real environment, not delivered as a disconnected demo.
- Embedded AI Engineer for Your Team
- From Prototype to Production, In Your Stack
In short
- Embedded with your team (on-site in Dhaka or remote), working in your repositories, cloud and processes
- Discovery with real users and data to find the AI use cases worth building
- Rapid prototypes in days, then hardening to production: evaluation, security, monitoring
- Integration with existing systems: databases, APIs, CRMs, internal tools and SSO
- LLM observability and dashboards (Langfuse, Grafana) so leadership sees usage and accuracy
What you get
Use-case discovery
Workshops and shadowing with real users to find what is worth automating.
Working prototypes
Prototypes on your real data within days, tested with the people who will use them.
Production deployment
Hardened, monitored AI services in your cloud and repositories.
Integrations
Connected to your databases, APIs, CRMs, identity and internal tools.
Knowledge transfer
Documentation, runbooks and training so your team runs it independently.
Key services & features
Scroll through what this service covers. Each capability is explained in technical depth further down.
Embedded with your team
Works in your standups, repositories and tools, not as an outside vendor.
Customer-facing discovery
Sits with users and stakeholders to understand the real problem.
Rapid prototyping
From idea to a working prototype on your data in days.
Production hardening
Evaluation, security, scaling and monitoring before wide rollout.
Systems integration
AI wired into the systems your business already runs on.
Observability for leadership
Dashboards showing usage, accuracy and impact to decision makers.
Technical deep dive
The methods and terms behind the work, for technical teams who want to know exactly how it is built. Open any group to explore.
What forward deployed means
The engineer goes to where the problem is: your data, your systems and your users.
- Embedded, not outsourced
- Part of your team's rhythm with direct access to users and engineers.
- Problem-first
- Starts from a business problem observed on the ground, not from a technology.
- Ownership to production
- Responsible for the outcome in production, not just a handed-over prototype.
- Feedback loop
- Learnings from real use flow straight back into the product.
How the work runs
Short cycles that show value early and reduce risk.
- Week-one discovery
- Map workflows, data sources and pain points; agree success metrics.
- Prototype sprints
- Small builds on real data, reviewed with real users every week.
- Hardening
- Evaluation sets, guardrails, access control, logging and load testing.
- Rollout
- Gradual release to more users with monitoring and quick fixes.
- Handover
- Runbooks, architecture notes and training sessions for your team.
Production essentials
What every deployment includes.
- LLM observability
- Langfuse tracing of every AI interaction across all pipelines.
- Dashboards
- Grafana dashboards on PostgreSQL for usage, latency and accuracy.
- Containerised services
- Docker images deployable to your cloud or on-premise servers.
- Security & compliance
- Data stays in your environment, with least-privilege access and audit trails.
How we work together
Discover
A call to understand the goal, the users and the data. I tell you honestly whether AI is the right tool.
Design
Scope, architecture, data flow and success metrics agreed in writing before any build starts.
Build
Iterative development with demos every week, so you see working software early and steer it.
Evaluate
Tested on real questions and real data against the agreed metrics, not just a happy-path demo.
Deploy
Shipped to your cloud or server with Docker, monitoring and documentation.
Support
Handover, training and optional ongoing improvements as usage grows.
Tools & technologies
AI
- OpenAI
- Gemini
- LangChain
- LangGraph
- Qdrant
Backend
- FastAPI
- PostgreSQL
- Docker
Observability
- Langfuse
- Grafana
Collaboration
- Git
- GitHub
- Your team's tools
Where this fits
Enterprise AI assistant
Embedded at Banglalink building Blink AI with retrieval, observability and dashboards.
University AI systems
AI Proctor, Smart Transport and photo search delivered inside DIU.
AI enablement for a team
Kick-start AI capability in a company that has no AI engineers yet.
Rescue a stalled AI project
Take a prototype that never reached users and get it into production.
Related projects
Real systems built with this service, each with a full case study.
Common questions
- What is a forward deployed AI engineer?
- An engineer who works directly inside a client's team and environment, close to the users and data, owning AI solutions from discovery through production.
- Can you work on-site?
- Yes, on-site in Dhaka or remotely, depending on what the team and project need.
- How is this different from hiring a vendor?
- A vendor delivers a scoped project from outside. A forward deployed engineer works inside your systems and team, adapts as needs change, and leaves your team able to own the work.
Have an AI problem worth solving?
Tell me what you are trying to build. I will tell you honestly whether AI is the right tool, what it would take, and how I would approach it.
Typical reply within 24 hours
Working from Dhaka, Bangladesh · remote worldwide