Machine Learning Engineer

Role Overview :

We are looking for an experienced

MLOps Engineer

who will bridge

AI / ML model development with production deployment and monitoring

, ensuring models are scalable, reliable, and performant in production environments.

Key Responsibilities :

Build and manage

MLOps pipelines

for AI / ML models (segmentation, Next Best Action, recommendations).

Deploy and monitor models across

Dev / SIT / PROD environments

Implement

model versioning, drift detection, and retraining workflows

Optimize

model inference performance

for low-latency responses.

Collaborate with infrastructure teams to ensure

hardware and cloud readiness for GenAI

Establish

observability and logging

for model behavior and system performance.

Required Skills : 7–10 years

of experience in MLOps or related roles.

Strong knowledge of

ML lifecycle management

CI / CD for ML

, and

model deployment frameworks

(e.g., MLflow, Kubeflow, Airflow).

Expertise in

Python

Docker

, and

Kubernetes

Experience with

cloud platforms

(AWS / Azure / GCP) and GPU-based infrastructure.

Familiarity with

monitoring tools

(Prometheus, Grafana) and

logging systems

Understanding of

model drift detection

and

retraining strategies

Good to Have : Exposure to

GenAI model deployment

Experience with

data pipelines

and

feature stores