Machine Learning Engineer

This

position

is

listed

on

behalf

of

a

partner

company,

who

manages

all

applications

and

next

steps.

Our

partner

is

looking

for

a

Machine

Learning

Engineer

based

in

India

.

As

a

Machine

Learning

Engineer,

you

will

contribute

to

the

development,

improvement,

and

evaluation

of

modern

machine

learning

systems,

with

a

particular

focus

on

Large

Language

Models

(LLMs)

and

transformer-based

architectures.

You

will

work

with

existing

machine

learning

codebases,

analysing

their

structure

and

implementing

practical

enhancements

to

improve

model

performance

and

reliability.

Using

Python,

PyTorch,

and

Hugging

Face

Transformers,

you

will

support

model

fine-tuning,

inference,

and

evaluation

across

a

range

of

AI

projects.

You

will

also

explore

prompt

engineering

techniques

and

investigate

model

behaviour

to

identify

opportunities

for

optimization.

This

role

offers

the

opportunity

to

work

on

advanced

AI

initiatives

involving

real-world

machine

learning

challenges.

With

a

flexible,

fully

remote,

part-time

arrangement,

you

can

contribute

independently

while

collaborating

on

innovative

AI

development

projects.

Accountabilities:

Understand

existing

ML

systems:

Review

and

navigate

established

machine

learning

codebases

to

understand

their

architecture,

functionality,

dependencies,

and

implementation

details.

Develop

and

improve

ML

implementations

Write,

modify,

debug,

and

optimize

machine

learning

solutions

using

Python

and

frameworks

such

as

PyTorch.

Work

with

modern

AI

architectures:

Implement

and

support

transformer-based

models

and

LLM

applications

using

Hugging

Face

Transformers

and

related

technologies.

Support

model

fine-tuning:

Apply

fine-tuning

techniques

to

adapt

existing

models

to

specific

tasks,

improve

their

performance,

and

enhance

output

quality.

Perform

model

inference

and

evaluation:

Run

experiments

and

evaluate

model

accuracy,

performance,

consistency,

and

overall

effectiveness

using

appropriate

evaluation

methodologies.

Apply

prompt

engineering:

Design

and

refine

prompts

and

explore

model

adaptation

techniques

to

improve

responses

and

task-specific

behaviour.

Troubleshoot

technical

challenges:

Investigate

model-related

issues,

debug

implementation

problems,

identify

performance

gaps,

and

recommend

practical

improvements.

Manage

development

workflows:

Use

Git

and

GitHub

for

version

control,

code

collaboration,

code

reviews,

and

maintaining

organized

development

processes.

Document

technical

findings:

Record

implementation

changes,

evaluation

results,

technical

decisions,

and

relevant

findings

to

support

project

continuity

and

knowledge

sharing.

Requirements:

Professional

experience:

At

least

2

years

of

experience

in

Machine

Learning

Engineering,

Applied

Machine

Learning,

AI

Engineering,

or

a

related

technical

discipline.

Python

proficiency:

Strong

hands-on

programming

skills

in

Python,

with

the

ability

to

develop,

debug,

and

maintain

machine

learning

applications.

Machine

learning

frameworks:

Practical

experience

with

PyTorch

or

comparable

machine

learning

frameworks.

LLM

expertise:

Experience

working

with

Hugging

Face

Transformers

and

modern

Large

Language

Models.

Model

architecture

knowledge:

Strong

understanding

of

transformer-based

architectures

and

the

principles

behind

modern

machine

learning

systems.

Fine-tuning

and

inference:

Practical

knowledge

of

model

fine-tuning,

inference

workflows,

and

techniques

for

adapting

models

to

specific

requirements.

Evaluation

capabilities:

Familiarity

with

model

evaluation

methodologies

and

the

ability

to

assess

accuracy,

performance,

and

output

quality.

Prompt

engineering:

Understanding

of

prompt

design

and

optimization

techniques

for

improving

LLM

behaviour

and

task-specific

results.

Codebase

navigation:

Ability

How Jobgether works

• We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
• We appreciate your interest and wish you the best!
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