AI Engineer (Senior)

Imizizi

Strong experience with AWS services for data engineering, including S3, Lambda, VPC, IAM, and CloudWatch.
Proven proficiency in Python (Python 3.x) and PySpark for building ETL and data-processing pipelines.[2]
Hands-on experience with data-store technologies relevant for data engineers, such as DynamoDB or other
NoSQL and relational databases.
Solid understanding of data engineering concepts: ETL/ELT, data modelling, schema design, and analytical
processing.
Experience building and maintaining CI/CD pipelines and automated testing (GitHub Actions or similar) for data
and ML workflows.
Proven use of Infrastructure as Code (Terraform/Terragrunt) to provision and manage cloud infrastructure.
Knowledge of containerisation and orchestration patterns (ECS, Step Functions, or similar) for production data
workloads.
Strong skills in monitoring, logging, and alerting for data pipelines and ML platform components (CloudWatch,
metrics, logs).
Practical experience integrating data workflows with MLOps pipelines and model lifecycle tooling (SageMaker,
SageMaker Pipelines or equivalent).
Strong problem solving, analytical skills, and ability to communicate technical concepts to both technical and non-technical stakeholders.

Experience with AI engineering workflows, including model serving, feature stores, and model observability.
Understanding of agentic systems and multi-agent architectures relevant to Agent Fabric-style platforms.
Familiarity with Amazon Athena, AWS Glue, and streaming technologies (Kinesis, Kafka) for real-time/near-realtime
pipelines.
Experience with feature engineering at scale and data preparation for ML teams.
Familiarity with Databricks or managed Spark environments and optimisation of Spark jobs.
Exposure to low-code/no-code tooling that assists business teams in data access and insights.
Familiarity with data modelling and SQL tuning for analytical workloads (Oracle SQL or equivalent).
Knowledge of security hardening and networking best practices in AWS for data platforms.
Experience mentoring junior engineers and leading cross-functional data integration efforts.
Familiarity with monitoring model behaviour and evaluating LLM outputs as part of quality assurance

Design, build, and operate scalable, secure data pipelines to support MLOps and Agent Fabric workloads.
Integrate diverse data sources and ensure robust data ingestion, transformation, and availability for ML teams.
Collaborate with ML Engineers and AI teams to productionise models and embed data requirements into
MLOps pipelines.
Implement Infrastructure as Code (Terraform/Terragrunt) to provision and manage platform components.
Build and maintain CI/CD pipelines and automated testing for data and ML deliveries.
Ensure operational excellence through monitoring, alerting, and logging of data workflows and models.
Participate in data modelling, schema design, and optimisation for efficient feature storage and retrieval.
Improve data security and networking posture across the data platform in collaboration with DevOps and
security teams.
Enable business users and analysts by supporting data access patterns, low-code solutions, and documentation.
Mentor and coach junior data engineers, sharing best practices in data engineering and MLOps.
Work in an Agile delivery model, contributing to planning, estimation, and delivery of features.
Evaluate and recommend tools and patterns for supporting agentic systems and AI-driven integrations within the Agent Fabric.

Degree in Data Science, Computer Science, Statistics, Engineering, or equivalent relevant experience
Minimum of 3-5 years’ experience in data science, AI applications, or related fields with demonstrated
stakeholder management experience
Proven track record of designing or enabling AI/ML/Data Engineering solutions and working with cross functional delivery teams to deploy them into production.

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