Job Overview

designation: - ml / mlops engineer

location: - noida (sector- 132)

key responsibilities:

• model development & algorithm optimization: design, implement, and optimize ml

models and algorithms using libraries and frameworks such as tensorflow, pytorch, and

scikit-learn to solve complex business problems.

• training & evaluation: train and evaluate models using historical data, ensuring accuracy,

scalability, and efficiency while fine-tuning hyperparameters.

• data preprocessing & cleaning: clean, preprocess, and transform raw data into a suitable

format for model training and evaluation, applying industry best practices to ensure data

quality.

• feature engineering: conduct feature engineering to extract meaningful features from data

that enhance model performance and improve predictive capabilities.

• model deployment & pipelines: build end-to-end pipelines and workflows for deploying

machine learning models into production environments, leveraging azure machine

learning and containerization technologies like docker and kubernetes.

• production deployment: develop and deploy machine learning models to production

environments, ensuring scalability and reliability using tools such as azure kubernetes

service (aks).

• end-to-end ml lifecycle automation: automate the end-to-end machine learning

lifecycle, including data ingestion, model training, deployment, and monitoring, ensuring

seamless operations and faster model iteration.

• performance optimization: monitor and improve inference speed and latency to meet real-

time processing requirements, ensuring efficient and scalable solutions.

• nlp, cv, genai programming: work on machine learning projects involving natural

language processing (nlp), computer vision (cv), and generative ai (genai),

applying state-of-the-art techniques and frameworks to improve model performance.

• collaboration & ci/cd integration: collaborate with data scientists and engineers to

integrate ml models into production workflows, building and maintaining continuous

integration/continuous deployment (ci/cd) pipelines using tools like azure devops, git,

and jenkins.

• monitoring & optimization: continuously monitor the performance of deployed models,

adjusting parameters and optimizing algorithms to improve accuracy and efficiency.

• security & compliance: ensure all machine learning models and processes adhere to

industry security standards and compliance protocols, such as gdpr and hipaa.

• documentation & reporting: document machine learning processes, models, and results to

ensure reproducibility and effective communication with stakeholders.required qualifications:

• bachelor's or master's degree in computer science, engineering, data science, or a related

field.

• 3+ years of experience in machine learning operations (mlops), cloud engineering, or

similar roles.

• proficiency in python, with hands-on experience using libraries such as tensorflow,

pytorch, scikit-learn, pandas, and numpy.

• strong experience with azure machine learning services, including azure ml studio,

azure databricks, and azure kubernetes service (aks).

• knowledge and experience in building end-to-end ml pipelines, deploying models, and

automating the machine learning lifecycle.

• expertise in docker, kubernetes, and container orchestration for deploying machine

learning models at scale.

• experience in data engineering practices and familiarity with cloud storage solutions like

azure blob storage and azure data lake.

• strong understanding of nlp, cv, or genai programming, along with the ability to apply

these techniques to real-world business problems.

• experience with git, azure devops, or similar tools to manage version control and ci/cd

pipelines.

• solid experience in machine learning algorithms, model training, evaluation, and

hyperparameter tuning