Data Annotator - Applied AI Engineer

This

position

is

listed

on

behalf

of

a

partner

company,

who

manages

all

applications

and

next

steps.

Our

partner

is

looking

for

a

Data

Annotator

–

Applied

AI

Engineer

based

in

India

.

The

Data

Annotator

–

Applied

AI

Engineer

will

contribute

to

the

development

and

improvement

of

advanced

artificial

intelligence

systems

through

specialized

technical

evaluation

and

data

annotation.

This

role

goes

beyond

traditional

annotation

work,

focusing

on

large

language

models

(LLMs),

generative

AI

applications,

and

intelligent

agent

workflows.

You

will

assess

AI-generated

content,

review

technical

implementations,

and

evaluate

the

accuracy

and

reliability

of

AI-powered

solutions.

Working

with

modern

technologies

such

as

Retrieval-Augmented

Generation

(RAG),

Python,

and

AI

development

frameworks,

you

will

help

improve

model

performance

and

reasoning

capabilities.

The

position

offers

the

opportunity

to

apply

practical

AI

engineering

expertise

to

real-world

model

training

and

evaluation

projects.

This

is

a

flexible,

fully

remote,

part-time

opportunity

suited

to

professionals

with

hands-on

experience

in

applied

AI.

Accountabilities:

Analyze

existing

AI-powered

applications,

review

their

implementation

logic,

assess

system

architectures,

and

evaluate

technical

correctness.

Evaluate

large

language

models,

transformer-based

architectures,

model

configurations,

and

LLM

integration

workflows.

Assess

Retrieval-Augmented

Generation

(RAG)

pipelines,

including

embedding

models,

vector

databases,

retrieval

mechanisms,

and

context-generation

processes.

Review

AI

agent

architectures,

tool-calling

mechanisms,

multi-agent

workflows,

orchestration

logic,

and

autonomous

task

execution.

Perform

specialized

technical

data

annotation

involving

prompts,

AI-generated

responses,

code,

agent

workflows,

and

technical

documentation.

Evaluate

AI-generated

outputs

for

factual

accuracy,

logical

reasoning,

contextual

relevance,

instruction

adherence,

and

technical

quality.

Review

and

assess

prompt

engineering

techniques,

system

instructions,

prompt

chains,

and

AI-generated

responses.

Examine

Python-based

AI

implementations,

identify

logical

errors,

assess

integration

approaches,

and

recommend

technically

sound

improvements.

Evaluate

LLM

APIs,

model

integrations,

fine-tuning

strategies,

inference

pipelines,

and

AI

model

deployment

workflows.

Assess

AI

applications

developed

using

frameworks

such

as

LangChain,

LlamaIndex,

and

other

generative

AI

development

technologies.

Produce

clear

technical

explanations,

structured

annotations,

justifications,

and

actionable

feedback

to

support

AI

model

evaluation

and

improvement.

Maintain

high

standards

of

annotation

accuracy,

consistency,

and

quality

while

following

project-specific

guidelines

and

evaluation

criteria.

Requirements:

2–6

years

of

professional

experience,

with

at

least

2

years

in

Applied

AI

Engineering,

AI

Engineering,

Generative

AI,

LLM

Engineering,

or

a

related

technical

field.

Strong

hands-on

programming

experience

with

Python.

Practical

experience

building,

integrating,

or

maintaining

applications

powered

by

large

language

models.

Strong

understanding

of

LLMs,

transformer-based

architectures,

and

generative

AI

concepts.

Experience

working

with

LLM

APIs

and

models,

including

OpenAI,

Anthropic,

Google

Gemini,

or

open-source

alternatives.

Practical

knowledge

of

Retrieval-Augmented

Generation

(RAG),

embeddings,

and

vector

databases.

Familiarity

with

AI

agent

frameworks,

tool

calling,

multi-agent

systems,

and

workflow

orchestration.

Experience

with

LangChain,

LlamaIndex,

or

comparable

AI

application

development

frameworks.

Understanding

of

prompt

engineering,

context

management,

and

LLM

evaluation

methodologies.

Familiarity

with

model

fine-tuning,

inference

pipelines,

and

AI

application

deployment

processes.

Ability

to

independently

read,

understand,

analyze,

and

evaluate

existing

AI

application

codebases.

Experience

using

Git/GitHub

and

collaborati

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.
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