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LLM Engineers

Hire Dedicated LLM Engineers

Add a vetted LLM engineer to fine-tune, serve and optimize the model layer for latency, cost and accuracy at your actual usage volume.

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LLM Engineers

Skills
  • Fine-tuning where it actually improves results
  • Caching to cut repeated inference cost
  • Cost tracked and kept predictable
  • Latency and cost reviews
  • Model choice matched to the task
  • Latency and cost optimized
Engagement models
  • Full-time dedicated
Stack
  • Base model
  • Serving layer
  • API endpoint
  • Caching
  • Fine-tune / adapter

Typical tasks

  • Model choice matched to the task and budget
  • Fine-tuning where it actually improves results
  • No lock-in to a single vendor or model
  • Serving tuned for your real traffic patterns
  • Caching to cut repeated inference cost
  • Cost tracked and kept predictable

Everything you need to run the engagement smoothly

  • Dedicated to your project

    The engineer works only on your account, not split across other clients' workloads.

  • Direct communication

    You talk to the engineer directly on your tools, with no account manager relay in between.

  • Flexible contracts

    Full time, part time, or a fixed scope of work. Scale up or down with notice, not a long lock-in.

  • Latency and cost reviews

    Serving performance and cost reviewed against agreed targets, not left unmonitored.

  • Timezone overlap

    Working hours arranged to overlap with your team for stand-ups and reviews.

  • No-cost replacement

    If the engineer is not the right fit, we replace them at no extra cost during the engagement.

Why teams hire LLM engineers through TCB Infotech

  • We run model workloads ourselves

    We serve language models in production for clients, so our engineers are tested on real latency and cost pressure, not trivia.

  • Fast, honest shortlisting

    You see 2 to 3 profiles, not a stack of resumes, and we tell you plainly if a fit is not right.

  • Direct access, no relay

    You manage the engineer's day to day work directly, on your tools and your process.

  • Setup you can keep

    Serving configuration and fine-tuning work are documented and handed over cleanly if the engagement ends.

  • No long lock-in

    Scale the engagement up or down with notice, based on your actual workload.

From requirements to a working engineer in about two weeks

  1. Share requirements

    Tell us your workload, current latency or cost pain points, and your usage volume.

  2. Shortlist

    We match engineers by serving and fine-tuning experience and send you 2 to 3 profiles.

  3. Interview

    You interview the shortlist directly and pick the engineer who fits your workload and team.

  4. Onboard

    The engineer reviews your current setup and starts tuning the serving layer in the first week.

  5. Scale as needed

    Add a second engineer or move to part time once performance and cost are stable.

  • AI team architect

    Designs the data, model and serving layers before your team builds.

  • Dedicated AI team

    A pre-matched team of four roles: architect, engineer, backend and frontend developer.

  • Dedicated AI engineers

    Vetted AI engineers who take a feature from raw data to a deployed, monitored system.

Hiring LLM engineers, answered

What does a dedicated LLM engineer do?

They select the model that fits your task and budget, fine-tune it on your domain data where useful, and build the serving layer that keeps latency and cost under control at your actual usage volume.

Does the engineer lock us into one model?

No. The approach is model-agnostic. The engineer chooses and configures whichever model fits your data, latency and budget, and can swap it later if a better fit becomes available.

How is this different from a generative AI engineer?

An LLM engineer focuses on the model layer itself: fine-tuning, serving, latency and cost. A generative AI engineer focuses on the pipeline around a content generation feature. If your need is a content pipeline rather than model infrastructure, see our Generative AI Engineers.

Can you reduce our current inference cost?

Often, yes. Common levers include right-sizing the model for the task, caching repeated requests, and tuning batch and concurrency settings on the serving layer.

What is the engagement model?

You can hire full time, part time, or for a fixed project. Contracts scale up or down as your model workload changes, with no long lock-in period.

What if the engineer is not the right fit?

We replace the engineer at no extra cost during the engagement, and hand over the serving setup and fine-tuning work to the new engineer.

Hire a Dedicated LLM Engineer

No commitment. See a shortlist before you decide.

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