Hire Machine Learning Engineers Who Ship Production ML Systems at Scale
The gap between a trained model and a production ML system is where most AI projects stall. HireWings recruits the engineers who bridge that gap — with the infrastructure skills, the systems thinking, and the ML depth to do it right.
5-10
Days to First Shortlist
3-5
Pre-Vetted Candidates
90-day
Replacement Guarantee
Machine learning engineering is one of the most demanding and least understood roles in the modern technology organisation. ML engineers need to combine software engineering excellence — system design, data structures, distributed systems — with a working understanding of machine learning sufficient to build reliable pipelines, serving infrastructure, and monitoring systems around the models data scientists produce. Finding people who are genuinely strong across both domains, rather than strong in one and adequate in the other, is a search that most recruitment approaches handle poorly.
HireWings recruits ML engineers across the full spectrum: engineers focused on the model training infrastructure and compute pipelines, those specialising in model serving and inference optimisation, and the MLOps specialists who build the CI/CD systems that make the entire ML lifecycle repeatable and reliable. We assess candidates across all relevant dimensions and present only those who meet the bar across both the engineering and ML axes.
Roles We Place
Why ML Engineering Hiring Is So Difficult
ML engineering sits at the intersection of two competitive markets — software engineering and data science — making it one of the most contested hiring categories in India.
Genuinely rare skill intersection
Strong software engineers who also understand ML deeply enough to build production systems around it are extremely rare. Strong data scientists who can build production-grade serving systems are equally rare. The Venn diagram of these two populations is very small.
MLOps is still maturing
The tooling, practices, and roles in MLOps have evolved rapidly. Candidates who learned MLOps on early frameworks may not be current on modern practices using Feast, Ray Serve, MLflow, or Kubeflow. Assessing current relevance requires ongoing engagement with the practice area.
Compensation is highly competitive
ML engineers are competed for by both technology companies and research organisations, creating a compensation environment where the best candidates have significant leverage. Companies that don't benchmark correctly lose offers at the final stage.
HireWings' ML Engineering Recruitment Practice
Our ML engineering searches combine targeted sourcing from ML-intensive product companies and research teams with a structured assessment framework that evaluates both engineering quality and ML systems thinking.
Targeted sourcing from ML-heavy product companies, AI labs, and tech-enabled enterprises
Dual-dimension assessment: engineering depth (system design, coding, distributed systems) and ML depth (model lifecycle, training infrastructure, serving, monitoring)
MLOps-specific evaluation for CI/CD for ML, experiment tracking, and model governance
Inference optimisation assessment for roles requiring latency-sensitive serving
Compensation benchmarking covering both engineering and ML market rates
Pre-engaged ML engineering pipeline across Bengaluru, Hyderabad, and remote-first talent
Ready to start this search?
Our consultants are ready to brief, source, and deliver — starting with a discovery call this week.
5-7 Day Shortlist
First curated shortlist delivered within a week of the discovery call for most roles.
3-5 Pre-Vetted Candidates
Every profile personally interviewed and assessed — not a database dump.
90-Day Guarantee
Free replacement search if the placed professional leaves within 90 days.
Frequently Asked Questions
Answers to the questions employers ask us most about this search.
Ready to build your team?
Let's discuss your hiring goals and how HireWings can deliver the talent you need.