programmatic
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Engineering partners

Hire LLMOps engineers

Evaluation, deployment and monitoring for LLM applications. Define the responsibilities, review relevant experience and bring a partner into your team with a clear first assignment.

Discuss your team needsExplore the engagement

Meet relevant engineering partners

Start with a conversation about the work.

Review the individual's relevant experience and discuss your scope together. Availability, responsibilities and working hours are confirmed for each engagement.

Portrait of Ahmer Sultan

Ahmer Sultan

Fractional CTO & Engineering Leader · AI-Assisted Delivery

Ahmer's focus is high-velocity product delivery, fractional CTO strategy, LLM operations, and forward-deployed solution engineering. Discuss your MVP roadmaps, production machine learning scaling boundaries, real-world customer integration workflows, or the deployment of generative engineering loops, then collaborate with Ahmer to architect and embed robust enterprise systems.

  • LLMOps & Model Deployments
  • Forward Deployed Engineering
  • AI-Assisted Development
  • Fractional CTO Strategy
Discuss fit ↗View LinkedIn ↗

Experience shown belongs to the individual. Programmatic client results are documented separately.

Inside the delivery

An example first assignment

Stand up an evaluation harness with versioned prompts and production monitoring for one LLM workflow. Use the following checkpoints to discuss scope and review the contribution.

Reference approachAdapted during discovery
  1. 01

    Define evaluation criteria

    Agree quality signals, representative test cases and acceptance thresholds for the workflow.

    Output

    An evaluation specification

  2. 02

    Version prompts and models

    Track prompt, model and retrieval configurations behind the application's controls.

    Output

    A versioned LLM configuration

  3. 03

    Evaluate quality and cost

    Check answer quality alongside latency, token cost and guardrail behaviour.

    Output

    Quality and cost evidence

  4. 04

    Plan release monitoring

    Track versions and the signals that justify review, rollback or human escalation.

    Output

    A controlled LLM deployment

Controls across the workflow

  • An evaluation specification
  • Quality and cost evidence

Decisions that shape the scope

What evidence should the partner explain?
Evaluation, deployment and monitoring for LLM applications. Review regression testing, guardrails and versioning. Discuss relevant work in your application context.
What context is needed before starting?
A role brief and architecture context for LLM evaluation, deployment pipelines and production observability. Confirm dependencies and the person responsible for acceptance.

Before you commit

Is this the right engagement?

Operationalise LLM applications with evaluation, deployment and monitoring. Review prompt and model versioning, retrieval quality and guardrails.

What we need from you
A role brief and architecture context for LLM evaluation, deployment pipelines and production observability.
How you accept the work
Stand up an evaluation harness with versioned prompts and production monitoring for one LLM workflow.
Scope & alternatives
This role extends your team. Agree responsibilities, partner availability and working hours in the proposal. For end-to-end project ownership, explore the related service below.

Overview

What this engagement covers

Operationalise LLM applications with dependable evaluation, deployment and monitoring. Review prompt and model versioning, retrieval quality, guardrails and cost-latency trade-offs. A role brief and architecture context for LLM evaluation, deployment pipelines and production observability.

Capabilities

Define the role around the assignment

Use these discussion areas to scope and evaluate the proposed contribution.

01

Evaluation and deployment for LLM applications

Scope the contribution around evaluation pipelines, prompt and model versioning, and controlled LLM deployment.

02

Technical discussion and review

Operationalise LLM applications. Review retrieval quality, guardrails, regression testing and quality-cost-latency trade-offs.

03

A practical first milestone

Stand up an evaluation harness with versioned prompts and production monitoring for one LLM workflow.

Frequently asked questions

Before extending your team

01

What should we evaluate when hiring LLMOps engineers?

Focus on evaluation design, prompt and model versioning, guardrails and quality-cost-latency trade-offs. Ask for a walkthrough of relevant work and the reasoning behind technical choices rather than relying only on a list of tools.

02

What could the first assignment look like?

Stand up an evaluation harness with versioned prompts and production monitoring for one LLM workflow.

03

How is this different from a managed project?

The specialist contributes to your team's backlog and agreed review process. A managed project assigns delivery of a defined scope to Programmatic. Decide which model matches the ownership you need before discussing staffing.

04

Are the engineers immediately available?

Availability and working hours are confirmed for the proposed partner during scoping. We do not promise a start date until the role, access and engagement terms are agreed.

05

How do partners use AI tools?

Agree permitted tools and data handling during onboarding. AI may assist implementation, tests or documentation, but outputs for LLM evaluation, deployment and monitoring still require engineering review and validation.

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Discuss your LLMOps engineers requirements

Share the responsibilities, collaboration hours and first milestone so we can discuss the appropriate contribution.

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