Mlops (1 Year Contract)

**About Capgemini Engineering**:
Capgemini Engineering is a global business line (GBL) focusing on Engineering and R&D services catering to Telecom & Technology Automotive, Aerospace, Railways, Medical Device, Industrial, Energy & Utilities sectors

Delivering engineering solutions for over 35 years, with a 50,000 strong team deployed across North America, Europe and APAC regions Capgemini Engineering is driving convergence of Physical and Digital worlds through technology, engineering and manufacturing expertise boosting competitiveness

Looking for a go-getter, out-of-box thinking and attitude to win in a competitive landscape.

**Roles and Responsibilities**:
MLOps Engineer sits at the intersection of Machine Learning, Software/Data Engineering, and DevOps. They integrate the best practices from each of these fields to ensure the effective deployment and management of machine learning models in production environments.
- Technical Leadership: Ability to recommend and advocate for MLOps design patterns, best practices, and tooling in an enterprise setting. Ability to lead the implementation of the same.
- Technical Debt Resolution: Address and resolve technical debt in current ML projects in production and incorporate best MLOps practices.
- Model Deployment: Deploy machine learning models and enhance automation in the deployment process.
- Automation and Checks: Implement and manage automation pipeline, set up necessary tests for continuous integration and deployment.
- Monitoring and Maintenance: Monitor data drift, model performance and other metrics in production and work with Data Scientists to retrain models and set up retraining pipelines.
- Security & Compliance: Ensure that ML systems comply with security standards and best practices in a cloud environment.
- Collaboration: Work closely with:

- Data Engineers and data modellers to understand the data pipelines and data models.
- Data scientists to understand experimental ML models and ensure that models are integrated and operationalized effectively.
- Software engineers/IT to deploy infrastructure.

**Required Skillset**:

- 7-10 years of overall experience in software engineering, data engineering, or MLOps preferably with enterprise-level, complex matrix organizations.
- Experience in setting up MLOps pipelines, systems and processes from scratch.
- Proven experience with AWS (Athena, Glue, ECS, EKS, VPC, etc.) and AWS SageMaker specifically for deploying machine learning models, enhancing automation and implementing necessary checks for continuous improvements.
- Develop and manage CI/CD pipelines (Azure Pipelines preferred) to automate model deployment, testing, and integration processes.
- Orchestration and monitoring of data pipelines and ML workflows, ensuring timely execution and monitoring (Apache Airflow preferred).
- Strong experience with Python and Bash for automating ML workflows, SQL and Pyspark for feature engineering.
- Familiarity with IaC tools such as Terraform or AWS CloudFormation for managing cloud infrastructure.
- Knowledge of security practices and compliance requirements for managing data and models in the cloud.Knowledge of Data Science/Machine Learning lifecycle and frameworks such as scikit-learn, Pytorch, Tensorflow.**Good to Have**
- Experience with Azure Synapse and Azure ML Studio
- Experience with Databricks
- Experience with dbt

**Let's talk about what's in it for you**:
Passionate people are Capgemini's Ace of Spades - join us to discover a career that will challenge, support and inspire you. Working at Capgemini you'll find the rewards are more than just financial. You will work alongside some very smart and inspiring people on exciting projects and you will also enjoy incredible benefits. We offer flexible work practices and 40 hours of self-development every year with a huge selection of learning opportunities to choose from.

As "Architects of Positive Futures", Capgemini actively supports the community in 3 ways:
**Diversity and Inclusion**:
**Digital inclusion**:

- at Capgemini we are using our skills to drive social impact initiatives focusing on helping society address the impact of the digital and automation revolution. We also provide employees with opportunities to give back to the community through charity projects and volunteer days.

**Environmental Sustainability**:

- Capgemini joined the CDP's (Carbon Disclosure Project) prestigious "A list" for its commitment to the Net-Zero economy. We are focusing on helping our clients transform towards more sustainable business models and committing to reduce our own carbon emissions (GHG) by 20% per employee by 2020.

Recognized by Ethisphere as one of the **World's Most Ethical Companies **for the last 8 years in a row, ethics and values are at the heart of Capgemini's corporate culture and business. Embedded in our DNA, our seven values - Honesty, Boldness, Trust, Team Spirit, Freedom, Fun and Modesty - have remained the same since company incepti