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Rivac AI Labs
Engineering & Software Development

AI Engineer

Turning a machine-learning idea into a feature that runs reliably in production is a different job from training a model in a notebook, it requires data pipelines, serving infrastructure, and monitoring that catches drift before your users do. We build the full path from data to deployed model: feature pipelines, model serving, and the integration work that makes an AI capability feel like a native part of your product rather than a bolted-on API call. This is for teams adding a genuine ML feature (recommendations, classification, personalization) not a general add-AI project without a defined outcome. You get a deployed, monitored model integrated into your actual product, with a clear owner for what happens when it starts drifting.

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. 1

    Diagnose the specific prediction or classification problem and what data is actually available to solve it.

  2. 2

    Build and evaluate a pilot model against held-out data before committing to a production serving architecture.

  3. 3

    Review model performance and failure cases with your team, including where the model should defer to a human.

  4. 4

    Deploy to production with monitoring, retraining triggers, and rollback paths built in from the start.

What You Get

Deliverables from this engagement

  • Deployed, monitored ML model integrated into your product
  • Data pipeline feeding the model in production
  • Evaluation report on model performance and known limitations
  • Monitoring dashboard for drift and prediction quality

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

Data Input

  • App Database Read
  • Feature Extraction Script
  • Sample Labeled Data

Simple Model Serving

  • Managed LLM API or Small Local Model
  • Python Inference Service
  • Prompt Template

Product Integration

  • API Endpoint Wrapper
  • Product Feature Call
  • Fallback on API Failure

Basic Monitoring

  • Manual Output Spot Check
  • Error Log Review
  • Weekly Accuracy Check

Cloud-Native / Managed Services Architecture

Managed Feature Pipeline

  • Managed Data Warehouse
  • dbt Transformation Job
  • Feature Store
  • Data Quality Check

Managed Model Serving

  • SageMaker or Vertex AI Endpoint
  • Autoscaling Inference

Managed Observability

  • Model Metrics Dashboard
  • Drift Monitoring Service
  • Alert on Drift

Product Layer

  • Application Backend
  • Feature Served to User
  • Fallback Rule Based Logic

Event-Driven & Automation-First Architecture

Data Event

  • New Data Event Arrives
  • Feature Pipeline Recompute
  • Data Validation Check

Training Automation

  • Scheduled Retraining Job
  • Drift Detection Trigger
  • Candidate Model Trained

Registry Gate

  • Model Registry
  • Eval Passes Threshold
  • Auto Promote to Staging

Automated Deployment

  • Deployment Pipeline
  • Auto Roll Out New Version
  • Automated Drift Alert

Enterprise-Grade & Compliance-Ready Architecture

Model Governance

  • Model Governance Registry
  • Lineage Tracking
  • PII Handling Policy

Fairness Gate

  • Bias and Fairness Audit
  • Sensitive Data Handling Review

Access and Audit

  • Training Data Access Control
  • Audit Trail of Model Versions
  • Deployment Approver Log

Resilience

  • Redundant Serving Regions
  • Rollback Plan for Bad Version
  • Compliance Report
  • Regulator Ready Export

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

Automated Training Pipeline

  • Automated Training Run
  • Automated Evaluation Suite
  • Training Data Snapshot

Candidate Report

  • Performance Metrics Report
  • Drift and Bias Report
  • Sample Predictions

ML Engineer Approval Checkpoint

  • Engineer Reviews Eval Report
  • Approve for Production

Live Serving

  • Model Promoted to Production
  • Live Serving Endpoint
  • Fast Rollback Path
  • Monitoring Dashboard Alert

Integration-Heavy / Ecosystem-Connected Architecture

Data Sources

  • Client CRM
  • Product Analytics
  • Data Warehouse
  • Support Ticket Data

Model Core

  • Feature Pipeline
  • Deployed Model

Feedback Into Client Systems

  • Scored Fields Written Back to CRM
  • Marketing Automation Trigger
  • Personalization Engine Update

Monitoring Integration

  • Slack Alert
  • PagerDuty Alert
  • Client BI Tool Logging

More in Engineering & Software Development

  • Prompt Engineer

    LLM prompt design and optimization that turns vague instructions into reliable AI behavior.

  • Multi-Agent Systems Architect

    Multi-agent pipeline design and governance, topology, context, trust, and failure recovery for agentic systems.

  • RAG Pipeline Engineer

    Production retrieval-augmented-generation pipelines: chunking, retrieval quality, hybrid search, re-ranking, and eval-driven tuning.

  • LLM Post-Training Engineer

    Domain-specific model post-training (SFT/DPO/GRPO/RLVR) with evidence-based experiment gating and checkpoint integrity.

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 AI Engineer

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

Questions? Book a free call