Job VC
Senior ML/Ops Engineer (Python / Real-Time ML Systems)
Technologies
Description
About the Role
We are looking for a
Senior ML/Ops Engineer
to join a core algorithmic platform team building
real-time ML inference systems at massive scale
.
This is a highly technical, hands-on role focused on designing and operating
production-grade ML systems
, not research. You will work on systems that handle
high throughput and strict latency requirements
, with full ownership across backend, ML, and infrastructure layers.
Responsibilities
Design, build, and maintain
real-time ML inference services
Develop and scale
backend systems in Python
Own
end-to-end ML systems
(architecture, services, pipelines, infrastructure)
Integrate ML models into
production services and business logic
Optimize systems for
low latency and high throughput
Work with
streaming systems and event-driven architectures
Ensure system reliability, monitoring, and performance in production
Collaborate closely with Data Scientists and engineering teams
Tech Stack
Python (core language)
Kafka / streaming systems
Kubernetes, AWS / GCP
FastAPI, Triton or similar inference frameworks
Feature stores, model serving, A/B testing, monitoring tools
High-performance data stores (Redis, Aerospike, etc.)
Requirements
Strong backend engineering background in
Python
Hands-on experience with
MLOps and production ML systems
Experience building and owning
end-to-end systems
Experience with
real-time / low-latency systems
Experience working with
high-scale environments
(large traffic, high throughput systems)
Strong system design skills (APIs, inference services, SLAs)
Solid understanding of the
ML lifecycle (training → deployment → monitoring)
Ideal Background
Started as a
Backend Engineer (Python)
and transitioned into ML Engineering / MLOps
Built
production-grade ML services
, not just pipelines
Experience in
high-scale domains
(AdTech, Fintech, Gaming, streaming systems)
Important Notes
This is a
high-ownership role
in a core team
The domain is complex and niche — focus is on
quality over quantity
Experience with
high-scale systems is highly preferred
Not a Fit If
Pure DevOps / Infrastructure profile
Only worked with ML pipelines (Airflow, ETL) without production ML
No experience with real-time systems or latency constraints
Mostly research or Data Science background without production systems
Hiring Process
2–3 interview stages
This role is ideal for engineers who combine
strong backend engineering with ML systems experience
and enjoy building
real-time, high-scale production systems from end to end
.
We are looking for a
Senior ML/Ops Engineer
to join a core algorithmic platform team building
real-time ML inference systems at massive scale
.
This is a highly technical, hands-on role focused on designing and operating
production-grade ML systems
, not research. You will work on systems that handle
high throughput and strict latency requirements
, with full ownership across backend, ML, and infrastructure layers.
Responsibilities
Design, build, and maintain
real-time ML inference services
Develop and scale
backend systems in Python
Own
end-to-end ML systems
(architecture, services, pipelines, infrastructure)
Integrate ML models into
production services and business logic
Optimize systems for
low latency and high throughput
Work with
streaming systems and event-driven architectures
Ensure system reliability, monitoring, and performance in production
Collaborate closely with Data Scientists and engineering teams
Tech Stack
Python (core language)
Kafka / streaming systems
Kubernetes, AWS / GCP
FastAPI, Triton or similar inference frameworks
Feature stores, model serving, A/B testing, monitoring tools
High-performance data stores (Redis, Aerospike, etc.)
Requirements
Strong backend engineering background in
Python
Hands-on experience with
MLOps and production ML systems
Experience building and owning
end-to-end systems
Experience with
real-time / low-latency systems
Experience working with
high-scale environments
(large traffic, high throughput systems)
Strong system design skills (APIs, inference services, SLAs)
Solid understanding of the
ML lifecycle (training → deployment → monitoring)
Ideal Background
Started as a
Backend Engineer (Python)
and transitioned into ML Engineering / MLOps
Built
production-grade ML services
, not just pipelines
Experience in
high-scale domains
(AdTech, Fintech, Gaming, streaming systems)
Important Notes
This is a
high-ownership role
in a core team
The domain is complex and niche — focus is on
quality over quantity
Experience with
high-scale systems is highly preferred
Not a Fit If
Pure DevOps / Infrastructure profile
Only worked with ML pipelines (Airflow, ETL) without production ML
No experience with real-time systems or latency constraints
Mostly research or Data Science background without production systems
Hiring Process
2–3 interview stages
This role is ideal for engineers who combine
strong backend engineering with ML systems experience
and enjoy building
real-time, high-scale production systems from end to end
.