MACHINE LEARNING

Machine Learning

Locations: DallasTX or AtlantaGA or SeattleWA Hybrid

Designing and implementing ML infrastructure and tools that support the endtoend 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 preproduction 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

3 years of work experience with MLOps lifecycle management

3 years of work experience with workflow platforms such as MLflow

3 years of work experience with Docker and containerization

3 years of work experience with Kubernetes and container orchestration platforms

3 years of work experience with Python Pyspark or Scala development

3 years of work experience with Azure AWS Google Cloud or other cloud computing platforms

3 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 to solve business problems and to 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 realtime 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 costefficiency. 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

2 years industry experience with work in a quant or data scientist field preferred

Masters degree or PhD in Computer Science Statistics Economics Mathematics or other closely related field

Excellent teamoriented and interpersonal skills with a strong interest for 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 modelling 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 Cloudbased environments such as AWS (S3/EC2) Azureand Google Cloud

Knowledge of other coding languages such as Java Matlab SAS C

Experience with building and deploying predictive and prescriptive analytics models