Machine Learning Engineer

MLOps Engineer (ML Pipelines \| CI/CD \| Kubernetes \| Cloud \| ML Observability)

We are looking for an experienced

MLOps Enginee

r to design, implement, and maintain scalable

machine learning pipeline

s and

production-grade ML infrastructur

e. This role is ideal for engineers who excel at bridging the gap between

data scienc

e and

production system

s, ensuring seamless

model deploymen

t,

monitorin

g, and

lifecycle managemen

t across cloud or hybrid environments

**.

Role Overvi**

ewAs an MLOps Engineer, you will build and automate end-to-end ML workflows, enabling reliable, repeatable, and scalable model operations. You will collaborate closely wit

h data scientis

ts

, ML enginee

rs, an

d software engineering tea

ms to productionize ML models with high availability, observability, and governanc

**e.

Key Responsibilit**

• iesDevelop and mainta

in CI/CD pipeli

nes for ML models, data workflows, and production servic

• es.Automa

te model train

in

g, validat

io

n, deploym

en

t, version

ing, a

nd rollback proces

s

• es.Impleme

nt monitoring, alerting, and observabil

ity for model performance, latency, a

nd data dr

i

• ft.Optimize ML infrastructure f

or cost efficie

nc

y, scalabil

it

y, performa

nce, a

nd high reliabil

ity across cloud or hybrid environmen

• ts.Work with cross-functional teams to integrate ML models in

to production syst

ems and enterprise applicatio

• ns.Ensure compliance wi

th secur

it

y, ML governa

nc

e, auditabil

ity, a

nd reproducibil

ity standar

**ds.

Required Skills \& Expe**

• rtiseStrong proficienc

y in P

ython and ML frameworks suc

h as Tenso

rF

low, Py

Torch,

and Scikit-

l

• earn.Experience

with D

oc

ker, Kuber

netes, and container-orchestration for scalable ML deploym

• ents.Hands-on experience with cloud platfo

rms

(

AWS,

Az

ure

, GCP) and ML-specific serv

• ices.Expertise with CI/CD tools suc

h as GitHub Ac

ti

ons, Je

nk

ins,

Argo, or similar automation t

• ools.Familiarity

with ML observab

il

ity, model regis

tr

ies, feature s

tores, and production monitoring t

• ools.Understandin

g of data versi

oning

and experiment tra

cking using tools

like M

Lflo

w o

r

**DVC.

Exp**

• erience5–

8 years of experie

nce in software engi

ne

ering, ML engi

neeri

ng, or data engi

n

• eering.M

inimum 3

\+ years of hands-on experie

nce i

n MLOps, ML pipelines, or production ML s

**ystems.

Preferred Qualif**

• icationsExperien

ce with large‑scale ML

systems, distributed training, and high‑performance compute envir

• onments.Familiari

ty with GenAI model de

ployment, optimization, and inference accel

• eration.Strong debugging, troubleshooting, and performance‑tuning sk

ills in production envi

r

onments.