Hire ML Engineers
Hire ML engineers who bridge the gap between research and scalable production systems.
35+
ML pipelines in production
6 yrs
average ML experience
48 hrs
to onboard your team
99.9%
model uptime SLA
What They Build
Key Capabilities of Our ML Engineers for Hire
End-to-End ML Pipeline Design
Architect training, validation, and serving pipelines using Kubeflow, Metaflow, or custom orchestration — reproducible and version-controlled from day one.
Feature Store Implementation
Build centralised feature stores (Feast, Tecton, or custom) that eliminate training-serving skew and enable feature reuse across multiple models.
Model Training at Scale
Distribute training across GPU clusters using PyTorch DDP, Horovod, or Ray Train — cutting training time from days to hours.
Real-Time Inference APIs
Deploy low-latency prediction services using TorchServe, Triton Inference Server, or FastAPI with auto-scaling and p99 latency SLAs.
Model Monitoring & Drift Detection
Implement statistical drift detection, data quality checks, and alerting so model degradation is caught before it impacts business metrics.
Experiment Tracking & Reproducibility
Set up MLflow, Weights & Biases, or Neptune so every experiment is logged, comparable, and fully reproducible by any team member.
Model Optimisation & Compression
Apply quantisation, pruning, and knowledge distillation to reduce model size by up to 10x and inference cost by up to 5x without sacrificing accuracy.
A/B Testing for ML Models
Design canary deployments and shadow mode testing to safely roll out new model versions and measure business impact before full release.
Engagement Options
Flexible Hiring Models for ML Engineers
Full Time
160 hrs/month
1 month minimum
- Dedicated full-time ML engineer
- Daily async standups
- Direct Slack/Teams access
- Weekly pipeline status reports
- Experiment tracking setup included
- Code reviews & documentation
- Full IP ownership transferred
- NDA & security agreement
Part Time
80 hrs/month
1 month minimum
- Dedicated part-time ML engineer
- Twice-weekly check-ins
- Async-first communication
- Bi-weekly progress reports
- Flexible scheduling
- Full IP ownership transferred
- Model monitoring setup included
- Ideal for ongoing pipeline maintenance
Hourly
Flexible hours
Pay-as-you-go
- No minimum commitment
- Scale up or down anytime
- Ideal for ML audits or sprints
- Hour tracking & reporting
- Direct engineer communication
- Same vetted talent pool
- Great for model optimisation tasks
- Pay only for hours delivered
Why Bluejay
Why Hire ML Engineers From Bluejay Advisory?
Machine learning engineers at Bluejay are not data scientists who learned Docker — they are engineers who deeply understand statistics and can architect systems that serve millions of predictions per second. From feature stores and training pipelines to real-time inference endpoints, our ML engineers build the infrastructure that makes your models reliable, reproducible, and cost-efficient in production.
True ML Engineering Depth
Our engineers understand the math behind the models and the systems that run them — avoiding the common pitfall of over-engineering or under-engineering.
Cost-Optimised Infrastructure
We design inference infrastructure with cost in mind — spot instances, batching, and model compression regularly cut cloud bills by 40–60%.
Seamless Team Integration
ML engineers work directly with your data scientists and backend teams, speaking both research and engineering languages fluently.
Security & Data Governance
Strict data lineage tracking, access controls, and audit logs built into every pipeline so you're always audit-ready.
Fast Iteration Cycles
Automated retraining pipelines and experiment tracking mean new model iterations go from idea to production in days, not weeks.
Long-Term Knowledge Transfer
We document every architectural decision and run knowledge-transfer sessions so your in-house team can maintain and extend the system independently.
How It Works
Hire ML Engineers in 4 Simple Steps
Share Requirements
Tell us the role, tech stack, experience level, and timeline. We'll clarify everything in a 30-minute call.
Review Candidates
Within 48 hours we present 2–3 pre-vetted profiles matched to your exact requirements.
Conduct Interviews
Interview the candidates directly — technical and culture-fit rounds on your terms.
Onboard & Kick Off
Selected developers integrate into your team, tools, and workflows within 48 hours.
Build your team with expert ML Engineers
Pre-vetted, senior talent — onboarded within 48 hours.
Technologies
Technologies Used by Our ML Engineers
ML Frameworks
MLOps Tooling
Serving & Infrastructure
Cloud Platforms
Explore More
Other Developer Profiles We Offer
Frequently Asked Questions
Everything healthcare organisations ask us before we start building together.
Ready to hire a ML Engineer?
Get matched with a pre-vetted ML Engineer and onboard within 48 hours.