ER&D Talent · MLOps & Production AI
Talent that keeps AI running in production
A model that works in a demo is not a product. Production AI needs engineers who can deploy it, watch it, and scale it under real traffic, cost, and compliance limits. Propellence finds the platform and reliability talent that a generic “ML engineer” search misses.
The production AI lifecycle: we hire for every stage, and for the loop that connects them
- Train & packagePipelines, registries, reproducible builds
- DeployCI/CD, safe rollout, rollback
- Serve & scaleLatency, throughput, GPU cost
- MonitorDrift, quality, incidents
- Govern & retrainAudit, security, feedback loop
Roles We Fill
Cross-domain MLOps roles, precisely defined
Generalist recruiters search by job title. We search by what keeps models running: release safety, latency, cost, drift, and uptime. That is often a hybrid profile no standard job description captures.
MLOps Engineer / Lead
Builds CI/CD for models, automates training-to-release pipelines, and makes rollouts and rollbacks safe and repeatable.
ML Platform Architect
Designs the shared platform for experiment tracking, model registry, orchestration, and self-service tooling that data science teams build on.
LLM Inference & Serving Engineer
Optimizes latency, throughput, and cost per request for large models through batching, quantization, caching, and autoscaling.
ML Observability & Reliability Engineer
Tracks data drift, model quality, and system health, and owns the alerting and incident response that protect production models.
Feature & Data Platform Engineer
Builds the feature stores, streaming pipelines, and data quality checks that keep training and serving data consistent.
Head of ML Platform / AI Infrastructure
Sets the production AI roadmap and owns build-vs-buy decisions across compute, tooling, cost, and governance.
Why Propellence
Built for a search problem that crosses domains
Most MLOps searches stall because the recruiting partner sources from one lane. The right candidate often sits between software engineering, infrastructure, and machine learning.
Bridge talent, not bucketed talent
We identify engineers who understand both the model and the system around it: people who can debug a drifting model and a failing pipeline in the same afternoon.
One search, six domains
Deployment, monitoring, inference, data platforms, GPU and cloud infrastructure, and governance, evaluated by consultants with real technical grounding in each.
Confidential, passive search
The engineers running production AI at scale are not browsing job boards. Our search is direct, discreet, and relationship-driven.
Global reach, where AI platform talent concentrates
Active coverage across India, Germany, the USA, Japan, and APAC, the hubs where product companies and GCCs are scaling AI infrastructure.
FAQ
MLOps talent acquisition, explained
Straight answers to what clients ask before starting a search.
What is MLOps talent, in hiring terms?
MLOps talent are engineers who deploy, monitor, and scale machine learning models in production. The roles span ML platform engineering, inference and serving, observability, data pipelines, and AI infrastructure, rather than sitting in one traditional discipline.
Why is production AI hiring harder than hiring data scientists?
It needs a rare mix of software engineering, infrastructure, and ML understanding. Most candidates come from one side, and few have run models reliably at scale under real traffic and cost pressure.
Do you hire for LLM serving and GPU infrastructure roles?
Yes. We search for inference and serving engineers, GPU and cloud infrastructure specialists, and ML platform leads for product companies, ER&D teams, and GCCs across India, Germany, the USA, Japan, and APAC.
How do you assess candidates for production experience?
Our consultants screen for the specifics: scale of traffic and models served, tooling used, incidents handled, and cost or latency results delivered, so you meet people who have run production systems, not only built prototypes.
Can you support a full ML platform team build-out?
Yes. From one critical platform architect to coordinated multi-hire team builds across the six production AI domains, with the same rigor we bring to GCC build-outs.
Scaling an ML platform or production AI team?
Tell us which capabilities are missing. We will bring you candidates who bridge them, not just fill a title.