ML Engineering

at  Capgemini

This position is Hybrid.

Job Summary:

• Designing and implementing ML infrastructure and tools that support the end-to-end ML development lifecycle.
• Developing and maintaining CI/CD pipelines for ML models and data.
• Collaborating with data scientists and engineers to understand their needs and help them develop, test, and deploy ML models, detect, and correct model drift in the data, enable pre-production testing, and ingest large volumes of structured and unstructured data for modeling.
• Optimizing the performance of ML models in a production environment.
• Ensuring security and compliance of ML systems.
• Strong Data Engineering skills.
• 1-2 years of work experience with MLOps lifecycle management.
• 1-2 years of work experience with workflow platforms such as MLflow.
• 1-2 years of work experience with Docker and containerization.
• 1-2 years of work experience with Kubernetes and container orchestration platforms.
• 1-2 years of work experience with Python, Pyspark or Scala development.
• 1-2 years of work experience with Azure, AWS, Google Cloud, or other cloud computing platforms.
• 1-2 years of work experience with Databricks, Snowflake, Redshift, or other cloud database management platforms.

Role & Responsibilities:

• Work in a collaborative environment with global teams to drive client engagements in a broad range of industries to design and build scalable AI and Machine Learning solutions, solve business problems, and create value by leveraging client data.
• Clean, preprocess, and transform raw data into a suitable format for machine learning models. This may involve tasks like data normalization, feature engineering, and handling missing values.
• Deploy machine learning models into production environments, ensuring scalability, reliability, and real-time performance. This may involve containerization, API development, and integration with existing systems.
• Assist in the design, development, and implementation of machine learning algorithms and models to solve specific business problems or improve existing processes. Support client and internal team members by contributing to coding, testing, and debugging tasks.
• Optimize machine learning algorithms and infrastructure for performance, scalability, and cost-efficiency. This may involve parallelization, distributed computing, and resource management.
• Collaborate with data scientists, software engineers, domain experts, and client stakeholders to understand requirements, gather feedback, and integrate machine learning solutions into larger systems or products.
• Stay updated on the latest advancements in machine learning, MLOps, and related fields, and apply new techniques and technologies to improve existing models or develop innovative solutions.

Qualifications:

• 1 -2 years of industry experience, with work in a quant or data scientist field preferred
• Master?s degree or PhD in Computer Science, Statistics, Economics, Mathematics, or other closely related field.
• Excellent team-oriented and interpersonal skills, with a strong interest in consulting.
• Outstanding communication skills with the ability to clearly articulate findings and present solutions to business partners.

Preferred Qualifications:

• Experience with one or two of the following: MLOps, Deep Learning methods, NLP, computer vision, sentiment analysis, topic modeling and graph theory, and databases.
• Experience with common data science tools such as Python, R, PyTorch, TensorFlow, Keras, NLTK, Spacy, or Neo4j, and a good understanding of modeling platforms such as Azure AutoML, SageMaker, DataBricks, DataRobot, and H2O.ai.
• Experience working with big data distributed programming languages, and ecosystems such as Spark, Hadoop, MapReduce, Pig, Kafka.
• Familiarity with Cloud-based environments such as AWS (S3/EC2), Azure,and Google Cloud.
• Knowledge of other coding languages such as Java, Matlab, SAS, C++.
• Experience with building and deploying predictive and prescriptive analytics models effectively.

• Work in a collaborative environment with global teams to drive client engagements in a broad range of industries to design and build scalable AI and Machine Learning solutions, solve business problems, and create value by leveraging client data.
• Clean, preprocess, and transform raw data into a suitable format for machine learning models. This may involve tasks like data normalization, feature engineering, and handling missing values.
• Deploy machine learning models into production environments, ensuring scalability, reliability, and real-time performance. This may involve containerization, API development, and integration with existing systems.
• Assist in the design, development, and implementation of machine learning algorithms and models to solve specific business problems or improve existing processes. Support client and internal team members by contributing to coding, testing, and debugging tasks.
• Optimize machine learning algorithms and infrastructure for performance, scalability, and cost-efficiency. This may involve parallelization, distributed computing, and resource management.
• Collaborate with data scientists, software engineers, domain experts, and client stakeholders to understand requirements, gather feedback, and integrate machine learning solutions into larger systems or products.
• Stay updated on the latest advancements in machine learning, MLOps, and related fields, and apply new techniques and technologies to improve existing models or develop innovative solutions