Engineering & Software Development

LLM Post-Training Engineer

When a general-purpose model isn't good enough at your specific domain task, the fix is often post-training (SFT, DPO, GRPO, or RLVR) not more prompt engineering. We run domain-specific post-training with evidence-based experiment gating, meaning every checkpoint is evaluated against real criteria before it's promoted, and checkpoint integrity is protected so a bad run doesn't overwrite a good one. This is for teams with a domain-specific task important enough to justify custom training, and with a real dataset or reward signal to train against. You get a model checkpoint that measurably outperforms the base model on your task, with the eval evidence to prove it.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

1
Define the domain task precisely and assess whether existing data or reward signal is sufficient to train against.
2
Run a pilot training pass on a smaller model or dataset slice to validate the approach before scaling up.
3
Review checkpoint evaluation results against baseline and gate promotion on evidence, not on-paper progress.
4
Scale to the full training run with checkpoint versioning and integrity checks protecting the promoted model.

What You Get

Deliverables from this engagement

Post-trained model checkpoint outperforming baseline on your task
Evaluation report comparing checkpoints against defined criteria
Training pipeline and configuration for future retraining
Checkpoint versioning and integrity verification setup

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Training Data

Curated Domain Dataset
Train Validation Split
Held Out Test Set

Single Run Training

Base Open Model
SFT Training Run
Checkpoint Saved Locally

Evidence Gate

Eval Set of Domain Tasks
Score Checkpoint Against Base Model
Checkpoint Beats Base

Release

Promote Checkpoint
Discard and Retry
Rollback to Last Good Checkpoint

Cloud-Native / Managed Services Architecture

Managed Data Pipeline

Domain Dataset in Cloud Storage
Data Validation Job
Train Validation Test Split

Managed Training Infrastructure

Managed GPU Training Cluster
SFT Stage
DPO Stage
GRPO or RLVR Stage

Checkpoint Management

Checkpoint Registry
Versioned Checkpoint Storage
Checksum Integrity Verification

Automated Evaluation

Automated Eval Harness
Experiment Tracking Dashboard
Gate Passed
Promote to Serving Registry

Event-Driven & Automation-First Architecture

Training Triggers

New Data Batch Available
Scheduled Retraining Job
Manual Experiment Submission

Automated Training Pipeline

Experiment Queue
Data Preprocessing Worker
Training Job Runner
Checkpoint Emitted Event

Automated Gating

Auto Eval Trigger on Checkpoint
Eval Worker Pool
Score Aggregator
Score Beats Current Best

Automated Outcomes

Auto Promote Checkpoint
Auto Archive Failed Run
Notify Team Channel

Enterprise-Grade & Compliance-Ready Architecture

Data and Experiment Governance

Approved Training Dataset With Provenance
Data Lineage Record
Experiment Proposal Review

Controlled Training Environment

Isolated Training Environment
SFT DPO or GRPO Run
Signed Checkpoint With Hash

Checkpoint Integrity

Immutable Checkpoint Store
Access Controlled Checkpoint Registry
Rollback to Last Known Good Checkpoint

Compliance and Audit

Bias and Safety Eval Suite
Human Sign Off Before Promotion
Full Audit Trail of Every Run
Compliance Reporting

AI-Augmented, Human-in-the-Loop Architecture

Automated Experiment Generation

Reward Model or Verifier Signal
GRPO or RLVR Training Loop
Candidate Checkpoint Produced

Evidence Based Gating

Automated Eval Suite Runs
Comparison Against Current Production Model
Statistically Significant Improvement

Human Review Gate

ML Lead Reviews Eval Report
Human Approves Promotion
Human Requests Additional Eval or Red Team

Gated Release

Checkpoint Promoted to Production
Checkpoint Rejected and Archived
Live Traffic Shadowing Before Full Rollout

Integration-Heavy / Ecosystem-Connected Architecture

Data and Signal Sources

Product Usage Logs
Customer Support Transcripts
Domain Expert Annotations
Existing Model Feedback Signals

Post Training Pipeline

Data Aggregation Layer
Training Orchestrator
Checkpoint Registry

Connected Ecosystem

Model Serving Platform
Experiment Tracking Tool
Feature Flag System for Rollout
Analytics Warehouse for Model Impact
Model Card Documentation Store

Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about LLM Post-Training Engineer

Most engagements like this start as a $500–$2,500 pilot, see full pricing.