Machine Learning Engineer / Lead (contractual)

Engagement Type :

Contractual (Immediate Joiners Preferred)

Location :

Noida / Gurgaon / Indore / Bangalore / Pune / Remote (Hybrid model for on-site locations)

About the Role

We’re seeking

experienced Machine Learning Engineers

to join our client’s high-impact AI / ML projects.

You’ll work on designing, developing, and deploying scalable ML solutions across domains — driving innovation, automation, and data-driven decision-making.

Key Responsibilities

Design, develop, and implement end-to-end

ML pipelines

including data ingestion, feature engineering, and model deployment.

Collaborate with business, data, and product teams to deliver actionable insights and AI-driven solutions.

Build scalable data pipelines using

PySpark

and integrate ML models into production environments.

Develop and fine-tune models for

forecasting, NLP, image / video analytics , and other advanced ML use cases.

Perform

exploratory data analysis , model performance evaluation, and hyperparameter tuning.

Implement

MLOps best practices

for model lifecycle management, versioning, monitoring, and CI / CD automation.

Leverage

AWS services

such as Sagemaker, Bedrock, and Kendra for model training and deployment.

Encourage a culture of continuous learning, experimentation, and innovation within the team.

Required Skills & Experience

Strong programming expertise in

Python .

3–10 years of hands-on experience in

data / feature pipelines

using

PySpark .

Proficiency in

ML model lifecycle , from data prep to production deployment.

Strong foundation in

statistics and probability

(hypothesis testing, distributions, regression, etc.).

Working knowledge of

MLOps tools and frameworks

for scalable deployments.

Exposure to

AWS Cloud

(Sagemaker, Bedrock, Kendra) and related services.

Familiarity with

technical architecture ,

model management , and

operational best practices .

Good to Have

Experience with

Generative AI and LLMs

(LangChain, LlamaIndex, Foundation Model Tuning, Data Augmentation).

Experience with

Docker and Kubernetes

for containerized deployments.

Hands-on exposure to

time-series modeling, forecasting, and advanced analytics .

Who Should Apply

Professionals passionate about solving complex problems using AI / ML.

Engineers available for

immediate or near-term joining .

Candidates open to

contractual / consulting engagements

with leading enterprise clients.