Job Description

Job description:

Job description:

About the Role:

We are seeking a highly skilled

Machine Learning Engineer

to design, build, and deploy scalable ML models and end-to-end AI solutions. The ideal candidate will have hands-on experience across the ML lifecycle -- from data preprocessing to model training, fine-tuning, evaluation, deployment, and monitoring. You'll collaborate with cross-functional teams to translate business problems into data-driven solutions and work with modern MLOps frameworks to ensure efficiency, reproducibility, and scalability.

Key Responsibilities:

Develop and implement

machine learning models

for structured and unstructured data. Perform

data preprocessing

, feature engineering, and exploratory data analysis using

Pandas

and

NumPy

. Design and maintain

end-to-end ML pipelines

for training, validation, deployment, and monitoring. Apply and fine-tune ML algorithms using

Scikit-learn

,

TensorFlow

, and

PyTorch

. Utilize

PySpark

for large-scale data processing and distributed ML workloads. Implement and manage

model deployment

using

AWS SageMaker

,

Azure ML

, or

GCP Vertex AI

. Use

MLflow

or similar tools for

experiment tracking, versioning, and reproducibility

. Monitor and optimize models for performance, drift, and scalability in production environments. Work with

Large Language Models (LLMs)

such as

OpenAI GPT

and

Hugging Face Transformers

for advanced NLP and generative AI use cases. Collaborate with Data Scientists, Engineers, and Product teams to integrate ML solutions into production systems. Contribute to

MLOps practices

, ensuring automation and efficiency across the model lifecycle. Stay up-to-date with emerging trends in ML, AI frameworks, and cloud-based ML solutions.

Required Skills & Qualifications:

Bachelor's or Master's degree in

Computer Science, Data Science, AI/ML, or related field

. 4-5 years of hands-on experience in

Machine Learning Engineering

or a similar role. Strong programming skills in

Python

with proficiency in

Pandas, NumPy

, and

Scikit-learn

. Expertise in

TensorFlow

,

PyTorch

, and

PySpark

. Experience building and deploying

end-to-end ML pipelines

. Strong understanding of

model evaluation techniques, fine-tuning, and optimization

. Experience with

MLOps tools

such as

MLflow

,

Kubeflow

, or

DVC

. Familiarity with

OpenAI

,

Hugging Face Transformers

, and

LLM architectures

. Proficiency with

cloud ML platforms

like

AWS SageMaker

,

Azure ML

, or

GCP Vertex AI

. Solid understanding of

model lifecycle management, versioning

, and

experiment reproducibility

. Excellent

analytical thinking, problem-solving

, and

communication skills

. Proven ability to work effectively in

cross-functional

and

collaborative

environments.

Nice to Have:

Experience with

data versioning tools

(e.g., DVC, Delta Lake). Familiarity with

containerization

and

orchestration tools

(Docker, Kubernetes). Exposure to

generative AI

applications and

prompt engineering

.

Why Join Us:

Opportunity to work on cutting-edge

AI/ML and LLM-based projects

. Collaborative, growth-driven environment. Access to the latest

AI tools and cloud ML infrastructure

. Competitive compensation and professional development opportunities.
Job Type: Full-time

Benefits:

Work from home
Work Location: In person

Job Types: Full-time, Permanent

Pay: ₹90,000.00 - ₹110,000.00 per month

Work Location: In person