Senior AI & Data System Engineer Needed

Senior / Staff Data & AI Engineer — Freelance

Engagement: Freelance / Contract
Work Arrangement: Remote
Level: Senior or Staff
Duration: Project-based, with potential for ongoing work

About the Project

We are looking for an experienced Senior or Staff Data & AI Engineer to help design, build, and productionize a data-intensive system involving cloud data platforms, machine learning, and AI-powered workflows.

This is not a task for someone who only creates notebooks, writes basic ETL scripts, or connects an API to an existing application. We need someone who can understand the full system, make sound architectural decisions, implement the difficult parts, and leave behind a maintainable production solution.

You should be comfortable working independently, explaining technical trade-offs, and taking ownership from initial architecture through deployment and operational handoff.

What You Will Own

Depending on the project scope, you may be responsible for:

Designing the overall data and ML system architecture.
Building reliable batch and/or streaming data pipelines.
Developing data models, transformations, and production data workflows.
Implementing machine learning models and integrating them into real applications.
Building AI/LLM workflows, including RAG, evaluation, and production integration where relevant.
Designing APIs, services, and orchestration for data and ML workloads.
Setting up monitoring, data quality checks, model evaluation, and failure handling.
Improving performance, reliability, scalability, and cloud infrastructure.
Handling sensitive data appropriately through access controls, encryption, and privacy-aware design.
Documenting architectural decisions and mentoring or collaborating with other engineers.
Technical Environment

Our expected stack may include:

Languages

Python
Advanced SQL
PySpark or Scala where appropriate

Data Engineering

Apache Spark
Apache Airflow
dbt
Kafka or other streaming systems
Data quality and validation frameworks

Cloud & Data Platforms

AWS or GCP
BigQuery, Redshift, Snowflake, or Databricks
Cloud storage, orchestration, and compute services

Machine Learning / AI

scikit-learn
XGBoost
PyTorch where relevant
MLflow or comparable MLOps tooling
LLM APIs, RAG pipelines, embeddings, and evaluation frameworks where relevant

Engineering & Security

Git
CI/CD
Docker
Testing and observability
IAM, secrets management, encryption, and PII-aware data handling

You do not need to know every technology listed. Strong production experience and sound engineering judgment matter more than keyword matching.

Required Experience

We are looking for someone who can demonstrate:

6+ years of relevant professional experience, or equivalent depth through substantial production work.
Experience designing and maintaining production data platforms.
Strong Python and SQL skills.
Experience building cloud-based data pipelines and data warehouses/lakehouses.
Experience deploying and operating machine learning models in production.
Strong understanding of data modeling, distributed processing, orchestration, and system reliability.
Ability to work with incomplete requirements and turn them into a practical technical plan.
Experience debugging real production issues rather than only completing isolated coding tasks.
Ability to explain architectural decisions, trade-offs, and implementation details clearly.
Strong written English and professional communication.
Strongly Preferred
Experience with Databricks, Spark, Airflow, BigQuery, AWS, or GCP.
Experience with fraud detection, forecasting, recommendation systems, churn modeling, or other business-critical ML applications.
Experience building RAG or LLM systems that are evaluated, monitored, and integrated into production workflows.
Experience with regulated, financial, healthcare, or otherwise sensitive data.
Experience implementing data privacy, access controls, auditability, or security-aware architecture.
Experience leading technical delivery or mentoring other engineers.
What We Expect You to Deliver

The exact deliverables will depend on the project, but may include:

A clear technical design and architecture.
Production-ready data pipelines and transformations.
Tested and deployable ML/AI components.
Integration with existing services or applications.
Monitoring, logging, and failure-handling mechanisms.
Documentation covering setup, operation, and key design decisions.
A handoff that another experienced engineer can maintain.
How We Evaluate Candidates

Please include:

Two or three relevant projects you personally built or owned.
The problem, system architecture, and your specific contribution.
The technologies used and why you chose them.
A difficult technical trade-off you made.
How the system was deployed, monitored, or maintained in production.
Any measurable outcome you can substantiate.
Your availability, hourly rate, and preferred engagement structure.

We are especially interested in candidates who can show real production ownership—not just a list of tools or a collection of tutorials and notebooks.

Engagement Details
Remote collaboration.
Flexible project-based engagement.
Potential for longer-term work depending on performance and project needs.
Candidates may be located internationally.
Availability during overlapping US business hours is preferred, but not mandatory.

To apply, send your profile, relevant project examples, availability, and rate.