Xerox

Job Role: Data Engineer

Job Description: A Data Engineer with AI/ML focus combines traditional data engineering responsibilities with the technical requirements for supporting Machine Learning (ML) systems and artificial intelligence (AI) applications. This role involves not only designing and maintaining scalable data pipelines but also integrating advanced AI/ML models into the data infrastructure. The role is critical for enabling data scientists and ML engineers to efficiently train, test, and deploy models in production. This role is also responsible for designing, building, and maintaining scalable data infrastructure and systems to support advanced analytics and business intelligence. This role often involves leading mentoring junior team members and collaborating with cross-functional teams.

Key Responsibilities:

Data Infrastructure for AI/ML:

• Design and implement robust data pipelines that support data preprocessing, model training, and deployment.

• Ensure that the data pipeline is optimized for high-volume and high-velocity data required by ML models.

• Build and manage feature stores that can efficiently store, retrieve, and serve features for ML models.

AI/ML Model Integration:

• Collaborate with ML engineers and data scientists to integrate machine learning models into production environments.

• Implement tools for model versioning, experimentation, and deployment (e.g., MLflow, Kubeflow, TensorFlow Extended).

• Support automated retraining and model monitoring pipelines to ensure models remain performant over time.

Data Architecture & Design

• Design and maintain scalable, efficient, and secure data pipelines and architectures.

• Develop data models (both OLTP and OLAP).

• Create and maintain ETL/ELT processes.

Data Pipeline Development

• Build automated pipelines to collect, transform, and load data from various sources (internal and external).

• Optimize data flow and collection for cross-functional teams.

MLOps Support:

• Develop CI/CD pipelines to deploy models into production environments.

• Implement model monitoring, alerting, and logging for real-time model predictions.

Data Quality & Governance

• Ensure high data quality, integrity, and availability.

• Implement data validation, monitoring, and alerting mechanisms.

• Support data governance initiatives and ensure compliance with data privacy laws (e.g., GDPR, HIPAA).

Tooling & Infrastructure

• Work with cloud platforms (AWS, Azure, GCP) and data engineering tools like Apache Spark, Kafka, Airflow, etc.

• Use containerization (Docker, Kubernetes) and CI/CD pipelines for data engineering deployments.

Team Collaboration & Mentorship

• Collaborate with data scientists, analysts, product managers, and other engineers.

• Provide technical leadership and mentor junior data engineers.

Soft Skills:

• Strong problem-solving and critical-thinking skills.

• Excellent communication and collaboration abilities.

• Leadership experience and the ability to guide technical decisions.

Core Competencies

• Data Engineering: Apache Spark, Airflow, Kafka, dbt, ETL/ELT pipelines

• ML/AI Integration: MLflow, Feature Store, TensorFlow, PyTorch, Hugging Face

• GenAI: LangChain, OpenAI API, Vector DBs (FAISS, Pinecone, Weaviate)

• Cloud Platforms: AWS (S3, SageMaker, Glue), GCP (BigQuery, Vertex AI)

• Languages: Python, SQL, Scala, Bash

• DevOps & Infra: Docker, Kubernetes, Terraform, CI/CD pipelines

Educational Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.

• 3 to 6 years of experience in data engineering.

• Strong understanding of data modeling, ETL/ELT concepts, and distributed systems.

• Experience with big data tools and cloud platforms.

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