Complete AI Forward Deployed Engineer(FDE) Roadmap With Usecases transcript

Hello everyone, my name is Krishna and welcome to my YouTube channel. So guys, today in this particular video we are going to discuss about the entire road map to learn about AI forward deployed engineer. I hope you have heard about this role AFD. Many many people are making amazing transitions to this specific role. If I talk about bigger companies like OpenAI, Claude, datab bricks, uh, Atlasian and many many companies who have their own AI product, they are definitely coming up with this prominent role and they're hiring people with amazing packages.

Right? So in this specific video, we are going to discuss about what does an AFD engineer basically do. Okay, we'll understand about the roles and responsibilities also. And the best part about this video is that I will be taking an amazing use case and I will be talking about how uh AI FDE engineer will specifically work. Right? So everything will be covered in this specific video. I think this video will go for 20 to 25 minutes.

Please make sure that you watch this video till the end because we are going to understand the crux of the specific roles and how do you actually become an AFD that also I'll be discussing about. So let me first of all go ahead. So here you can see the complete 6 months road map from fundamentals to shipping production AI inside real enterprises plus a full client case study. We are going to discuss about this also. Okay.

So let me first of all talk about what is a AI forward deployed engineer. Right. So the answer in a very simple way is that a engineer who sits with the customers and turns a foundational model into a business working system. Okay. working business system. Now this is really important uh and I'll talk about it. Let's say that there is a client right there may be a bank. There may be some other clients who are already having some amazing product.

Now the thing is that since in this era every company needs to implement something related to AI right how they can efficiently implement them within their legacy system that is the most important thing and AID is just like an experty who can actually help you with all those kind of work right that is the smallest definition that I would like to give right so here you can see forward deployed deployed forward into the client's environment the role was popularized by Palanteer and is now core at OpenAI, Anthropic, Scale AI, data bricks, Cohair and most AI native startups right so if you see right bigger bigger startups who are currently coming up right they are definitely coming up with this specific role and even for those companies who are who have you know amazing some products right they have already built it in the legacy system and they really want to integrate it they're also hiring you know FDE within themselves so that they can probably go ahead and implement AI integration within in them and see that how they can efficiently use the system.

You own the last mile, the grab between a model demo and a system that a bank, hospital or factory actually runs every day. Half engineers, half consultant fully accountable for the outcome. Now you may be seeing guys u you may be doing and see some people who are already working in a data science field as an AI engineer right may be working as an AI for FD but you may not be knowing. So that's the reason I really want to probably go ahead and explain everything over here with respect to the roles and responsibilities right.

So here you can see as an AIFD you build right you embed embed basically work on site inside Slack speaks to CIO and ops teams right then you own the outcome right so this is really really important let me hide my face so that you can see it you own the outcome measured on business KPIs not tickets closed then feedbacks right feedbacks bringing fields learning back to the product and research and you keep on doing that iteration until you get a final product which suitable for the business.

Right? Now let's go to the next slide and let's talk more about it. So here you can see how the FDE differs from adjacent AI roles. Right? So first of all I hope you have heard about this role AIML engineers then you have data scientist then you have solution engineers and then finally you have this AI forward deployed engineer. So in the case of an a IML engineer you basically uh sit along with the product or the platform team right as a data scientist you sit with the analytics team solution engineers are like pre-sales with sales right and AI forward deployed engineers you can see that it sits inside the customer organization right because you're actually solving a problem for them right and that's the reason you have this specific thing then what is the primary output right over here you have features you have model serving you try to build the best model and you know you probably go ahead and uh show them how you can actually do the inferencing how you can actually get the output in case of data scientists insights model experiments in case of solution engineers you probably provide demos PC's RFP answers many things but in the case of AI forward deployed engineer you actually work with the production system in the client environment okay um with respect to the integration let's say if you have a legacy system how you are basically going to integ integrate how you're going to make sure that security is one of the core issue that is basically solved evaluation many more things itself then you have success metric right in success metric in the case of AI I am engineer you basically talk about latency accuracy uptime uh in case of data scientist you talk about model quality lift you know and there is something called as drift also like how better you can actually make the model solution engineers is basically focusing on whatever demos they are actually providing right they're making sure that u you know the clients are going going to use it or not.

But here if you see it is all about client business KPI right cost time revenue right this KPI they basically talk about you know after integrating AI into their product how they are actually going to get beneficial with so they focus on the key performance indicators over there in case of customer contact uh daily you know post sale through go live daily you are in touch with the client because you are actually building it for them you are the uh AI I expert over here I can basically say AI expert and you're telling the um client to basically go ahead and do that if I talk about ambiguity level very high you define the problem key extra skill like discovery scoping change management we'll talk about this as we go ahead right but I hope you're getting an idea when we compare with some AI roles right so here you can see same toolbox but it's a complete different job when we compare it with the IML engineer right [snorts] now why this role is exploding around right now very simple right if I talk about open AI if I talk about anthropic if I talk about so many different different models let's say deepse everything kim right every foundational model lab AI startup has hit the same wall right that is the demo production gap models are commoditized what enterprises pay for is uh someone who can make them work with messy data legacy system and compliances revenue depends on it right AI lab sales multi-million dollar enterp enterprise contracts those contracts only renew if the deployment actually delivers value right so see uh if openAI is basically building a model or anthropic is building a model the next thing is that how do we specifically use this model in any enterprise that is the main thing and that is what a demo production gap is all about let's say if an open AI there is an AIFD and he or she will be able to tell the client that come I will show you that how you can integrate any open AI models with your legacy system and we are Making sure that we give you the best benefit wherein the revenue will be definitely high you know the expense will be less then definitely you can do a lot many things you get an opportunity to work with the business right then revenue dependent on talent is scarce right very few people can code architect and also sit from a CIO the scarcity is why FD's compensation at the top of the engineering brands I feel right now if any company wants to probably go from the legacy system to something in the current nature what is actually going in the system they really want to integrate AI, I think FD will be the kind of role who will be able to do it, who will be able to guide it and trust me, FD roles, you know, they get a huge amount of money in terms of the company, right?

Let's say OpenAI, if they're providing AI FD services to some other clients, they definitely take a lot of charge, right? So, let's talk about the five skills pillars of an AIFD because this now is with respect to technical aspects. how you can actually become an AFD that is what we really need to focus on right so first is uh about software engineering you definitely need to be good at programming language APIs gets testing docker SQL non-negotiable base that this is must you should actually know this so if you also planning to begin right you really need to be good at this because tomorrow if you are actually working with any of the clients you need to understand their legacy system how their APIs are each and everything Right?

And that is how things will go ahead. Then you have this LLM and agent engineering, right? Prompt, rag, structure, output, tools, agent evals. Now this is why important first you should know how to actually work with LLMs. You should know how to build AI agents, agent engineering and all right uh rag structured output tools. And since you need to incorporate all these things within the legacy system, so you should also know about evaluations, you should know about all the security terms. what are the compliances issues that you may probably facing with the client right the client may be facing and how do you actually go ahead and fix it then you have deployments and MLOps cloud container I'll also suggest instead of LLM ops I'll also MLOps I'll also suggest LLM ops you should know about cloud container CI/CD observative cost and latency control right uh enterprise integration here you'll be able to see o data pipeline security compliance legacy systems you should definitely know about let's say uh one of the company one one one client may be a bank right their legacy system uh data injection may not be available right how do you go ahead and do the data injection how do you go and bring all the data at one place let's say you want to go ahead and build a rack for them you have to make sure that you need to take care of all the security costings thing each and everything right then you have something called as consulting and product discovering scope stakeholder management driving adoption see according to me what AIFD is right it's a role of product engineer right plus AI engineer plus a solution architect.

This is what I feel right this is what I actually feel about right this role is a combination of multiple things why I'm saying product engineer because business analyst product engineer you need to keep on talking to the client so these are the five skills pillars of an AI FD okay now let's talk about the six months road map at a glance right 15 20 hours per week uh uh first month is all about foundation you focus on the foundation wherein you cover important things like Python, SQL, API, Git, dockers, LLM basics and everything.

Then you go ahead with LM engineer this two this three months right mostly most of the AI engineers also do it right AI engineer also has the same road map till the 3 months right u and probably some of the things AI engineer needs to also have if they really want to become an AI every then you go towards agentic AI tools use langram mult MCP multi- aent guardrails then you have guard security how do you fix it evals many more things right so LLM engineering agentic AI then This is the core time how you can actually transition yourself from an AI engineer to AIFD.

You need to probably think about production deployment, fast API, cloud, CI/CD, observability, gateways, enterprise integration, o data pipeline, security and all. Then FD, craft and capstone this is also there right. So here you build the skills here you integrate everything with the you get to know about the clouds you you get a chance to know how how the legacy system works. you get an idea about how to integrate with the um legacy system and build a perfect product right so here it is basically becoming deployable right and this is what it is all about now the next thing is that after this uh if you see with respect to the foundation month one like in foundation what all things we really need to focus so week one is all about python for production then week two is all about data and APIs week three is about dev toolings like git branching PR code review Linux C cla ssh Docker images.

Week four is all about LLM fundamentals. So as a manager, you really need to talk with clients. You need to probably have a lot of discussions what things they are actually building. You need to have an experience of talking with the cloud team, the ops teams and many more things. Right? Then the month is all about LLM engineering. The gold is to build reliable LLM features not demos and prove reliability with eval right.

Evals is one of the very important thing like how safe your system is. So week one is all about prompt engineering. Then week two we go ahead with understanding embedding and retrievalss rag different types of rag agentic rags many more things. Then we go ahead with evaluation which is core very important thing right here we have different different types of evaluation technique regression test on prompts and everything and finally you know here you should try to build some enterprise product okay enterprise product and that is how you get an idea about an enterprise product.

[snorts] Third month we basically go ahead with agentic AI. So here we discuss about agentic foundation frameworks interrupts and context safety and control you know these are like I've been teaching in my YouTube channel all the specific things. So see in my YT channel right I've almost taught everything in courses right in courses that we do specifically in live boot camps Udemy we have we have covered everything's in parts and pieces but soon we are also coming up with a dedicated AI FDE course that I will be announcing at the last so it'll be very important for you please make sure to watch this video till the end right then u here with respect to the agents here you should you are actually learning about react loop planning reflection tool design tool schemas memory short-term, long-term u frameworks like lang graph, lang chain, if you want uh Google ADK, cloud agent SDK, open agent SDKs, all these things you can actually learn interop context everything you will be able to learn.

Uh week four safety control and all you will be able to learn it over here. So till you go to the week four you will definitely be able to build some amazing projects because here uh you include everything like guardrails permission scoping loop limit budget quil situ how to use routers agentic routers many more things and such right and here your portfolio project should be about ops workflow agents multi-step agent that reads tickets queries DBs this is a kind of thing and here also you can see human approval is one very important features uh this is what we specifically do in agentic AI so that you get ready for the next step.

Right now coming to the production and deployment here uh there are four key terms that you should definitely learn about. One is serving and infra delivering pipeline delivery pipeline observability and LM gateways and scale right so here you can see some of the important tools and frameworks that we focus on is fast API docker right you have AWS Azure GCP you have GitHub action CI/CD environments how you should understand about dev staging and prod and this is what you specifically do in even those client companies you talk about secret management terraform uh tracing with lang token and cost dashboards online evals and feedbacks Then we talk about LLM gateways and scales.

All these things basically gets covered. And finally after this in the month four right you build some amazing cloud deployed agent platform. All right and all in our courses right you will be able to see the AFD course that we are going to launch right we are making sure to make the syllabus like this. Okay. Uh then you have something called as enterprise integration. Here the goal was to ship an AI system that stays up cheap and uh you know basically it uh I'll just rub this okay so that okay uh ship an AI system that stays up stays cheap and stays observable.

If you see in enterprise integration the goal is to make AI work with systems data rules and company a real company already has right. So here in the week one you can see identity and access you have data integration you have security and compliances you are you basically have the deployment models right um openw models let's say you want to go ahead and deploy do finetuning cost modeling right private endpoints uh you have to make sure that let's say your data need not be exposed to any LLMs how do you specifically do then you have SSO ooth SML basics audit logging connectors let's say you have multiple data points like shareepoint salesforce uh SAP, Jira, S3, how do you actually go ahead and do the connectors, how to probably go ahead and design ELT, data quality, PII handling and many more things.

And then you also have security and compliances, right? So GDPR is one very important thing that really needs to be taken care by many many companies. So that's the reason uh you also need to basically know this. And finally here you build a secure enterprise co-pilot and that is what we basically focus on. So this was about enterprise uh uh integration and finally you have FD in craft and cap capstone learn the consulting side of the job package yourself for the interview discover and scoping dis delivery playbook communication career launch right so this is the final thing that we should definitely do in our road map okay uh and finally you build a end toend client capstone so this many projects see the projects have been defined based on the uh months that we are specifically working on.

Right? So this is what a realistic week study cadence looks like. I will anyhow share this the FD tool stack language and core LLM agents retrieval serving and ops and quality and enterprise all these things are there uh how an AI FD solves a real business problem. Now let's talk about this. Okay, I will be taking a business use case over here and just to give you an idea like what an AFD basically do. We'll talk about it.

Okay, so illustrative a midsize general insurer drowning in claims paperwork. Okay, so this is the problem statement. The client let's say I've just taken one client Novashure general insurance. I don't know whether this insurance is available also or not. So they provide motor health insurance. Uh they have 200 1200 employees rigory core system uh three regional claim centers 42,000 claims they're getting every month 7.5 days average settlement time 180 plus claims uh plus claim adjuster and 202% claims accelerated or rework okay [snorts] now here you'll be able to see what is broken okay what is the problem we are trying to solve every craves as a bundle of PDFs photos emails adjusters manually ually retake data into the core system.

Policy wording lives into 400 plus documents. Coverage checks depends on adjust memory. Fraud rise is inconsistent across the three regional centers. Compl customer complaints about turnaround time are rising. Regulatory has flagged settlement SLAs. Right? So this is the problem that is basically there. What they have already tried a vendor chatbot demo that look great on simple claims and failed on rail scans. [snorts] an internal RPA project that broke every time that the core uh I will just hide my face again.

So here you can see an internal RPA project that broke every time the core system UI changed. Hiring more register the CI now wants to partner who will own the outcome and not ship a demo. Right? So here is where AI FD will come because they need to integrate AI and this all use cases can be solved with the help of AI. Week one and two how the FD runs the discovery and not develop. First of all, they will sit with the register in one center for 3 days.

Map 3 days is just one term. Okay. Map the 11 steps claim journey where the time is actually lost. Then they audit the data. Then they pick the KPI risk early scope the SOA scope of work, right? Um [clears throat] PC in 2 weeks, pilot six, production in eight, clear success criteria and exit condition. So they go ahead and do that and then they decide a specific architecture like int extract policy rack triage agents human review code system because they need to fix they need to work along with the legacy system they cannot break the legacy system right and this is how they basically go ahead with now the next thing is that about the platform layer right they should be built by the FD but owned by the client so models what models they should basically use let's say one example is Azure open GP class open weight fallback ILM then orchestration is there then data is there right Azure blob PG vector increment policy indexing governance side what all things they really need to do SSO RBC PI masking full tracing langu all these things when they actually do then only you basically get an enterprisegrade application and you are changing this legacy system into an enterpriseg grade application this entire thing will be built by an MD after communicating but it will be owned completely by the client Then roles and responsibilities you can go ahead and read down.

I have probably put up everything Pilot production how step by step you go. Then outcome after 16 weeks you'll be able to see all these things. Um this is what a entire road map of AIFD looks like and there are a lot of things that is basically there right over here and we have almost covered everything how you can actually go ahead and become a uh full stack afd itself. Finally guys uh we are coming up with this amazing boot camp that is AI forward deployed engineer.

It's launching on September 26th uh 27 2026. Here we are going ahead and we'll be teaching you how to actually become an AID. We'll follow this specific road map that we have defined. We'll develop those kind of use cases in front of you and we'll go ahead and probably explain you each and everything. Right. Uh over here you'll be able to see this is the um CL uh URL. You can go ahead and check it out. You can see like how detailed everything we will be covering like Python Linux, modern API development, cloud fundamentals, networking, LLM fundamentals, vector search, um agentic frameworks and lang graph everything over here.

Right? So you'll be able to see we'll also be developing some amazing good use cases. You can go ahead and check it out. Uh this batch is basically going to get started on September 27, 2026. So I hope you like this particular video. Uh this was it for my side. and I'll see you in the next video. This entire PDF I will be sharing in the description of this particular video itself. Right? So yeah, this was it from my side.

I'll see you in the next video. Bye-bye.