Hire an AI/ML developer from Netofficials and add machine learning or LLM-integration skills to your roadmap without rebuilding your process. We shape the role around your data, models and integrations, and you approve the fit before committing. The engineer who scopes the work leads the build. Send us your brief to start.
You get an engineer who works from your data, your success measure and your product constraints, not a generic model demo. We scope the problem first: classification, forecasting, recommendations, search or LLM features. Then we build, evaluate and tune against the results you care about. We will say if a simpler rule-based approach would serve you better.
Your engineer connects language models, retrieval and ML services to the APIs, databases and back end you already run. Prompts, model calls, fallbacks and data flows are built to fit your existing service contracts, so AI features slot into the product instead of sitting beside it as a separate experiment.
A model only helps once it runs reliably. Your engineer works alongside a cross-functional team covering back-end, QA and DevOps, so evaluation, release and deployment access are planned with the build. At release you receive documentation, and you can see progress at every sprint review.
You describe the product, the data and models involved, and the skills you want, such as machine learning, LLM integration or back-end work. We reply with a proposed fit for your roadmap.
You receiveYou confirm the role and the skills needed
You and the technical lead go through how the engineer's experience matches your data, models and integrations. We agree role expectations and a technical review approach before anyone is committed.
You receiveYou approve the proposed fit
We set up tool access, code and data ownership, and the communication window and update cadence. IP assignment is confirmed in the contract before work starts.
You receiveYou sign off the onboarding plan
Your AI/ML developer begins on a shared backlog with sprint reviews, working alongside your existing team. You speak directly to the technical lead throughout.
You receiveYou review progress at each agreed checkpoint
Send us your product, the models or data work involved, the skills you need and your working hours, and we reply with a proposed fit and the next steps before you commit to anything.
A long-term, full-time remote squad built around your machine learning roadmap, suited to products where models, data pipelines and integrations keep evolving.
A single AI/ML engineer who joins your existing team, suited to filling a specific gap such as model development or LLM integration.
A fixed-price build with a defined scope, suited to a bounded AI/ML piece such as a prediction feature or a document-processing workflow.
We quote after reviewing the role and the scope, and we publish no fixed rate on this page. The main cost drivers are the type of AI work (model training, LLM integration or a mix), the quality and location of your data, the systems the developer must connect to, the engagement model and the team around the engineer, such as QA and DevOps. You can choose fixed-price project delivery, a dedicated India-based team or staff augmentation. Share your brief and we will recommend the model that suits the work.
Nothing is committed until you approve the proposed fit, which comes after the technical review and the agreed role expectations. You own the outcome too: IP assignment is confirmed in the contract before the build begins, and you receive documentation and deployment access at release. The senior engineer who scopes your work leads the build, and you speak to that technical lead directly, with no account manager in between.
Yes. We place the engineer inside your tools, sprint rhythm and review process, whether that means a single augmented developer or a squad covering back-end, QA and DevOps. Before work begins we agree the communication window, meeting schedule and update cadence with you, so overlap hours are fixed rather than assumed. If your project needs a different approach, such as a fixed-scope build, we will say so after scoping.
Tell us the product, the models or LLM integrations on your roadmap, and your working hours. The senior engineer who scopes the role also leads the build, so you talk directly to the person accountable for it. Nothing is committed until you approve the fit, and cost is quoted only after we review the role and scope. If another engagement model or approach would suit your data and team better, we will say so after scoping.
