Find out exactly what skills, experience, and qualifications you will need to succeed in this role before applying below.
Duration:
12 month long contract +extensions
Compensation:
$65.00 - $70.00/hr
Location : 2 days onsite in Houston, Tx
Job Summary
We are seeking a highly experienced Senior AI Developer with strong Databricks expertise to lead the design, architecture, and implementation of scalable AI and data solutions. This role goes beyond development and requires ownership of end-to-end solution architecture within the Databricks Lakehouse ecosystem, ensuring high performance, scalability, and enterprise readiness.
Key Responsibilities
Design and architect end-to-end AI/ML solutions on the
Databricks Lakehouse Platform .
Develop and optimize scalable data pipelines using
Apache Spark and Databricks .
Define and implement
data architecture standards , including Delta Lake and Unity Catalog.
Build and manage
end-to-end ML pipelines
(data ingestion, training, deployment, monitoring).
Establish and implement
MLOps frameworks
using MLflow and CI/CD pipelines.
Collaborate with business stakeholders to translate requirements into scalable AI solutions.
Lead technical design discussions and provide architectural guidance across teams.
Ensure
performance optimization, cost efficiency, and reliability
of Databricks workloads.
Design
batch and real-time processing systems
(streaming, event-driven architectures).
Integrate advanced AI capabilities such as
LLMs and Generative AI
into enterprise solutions.
Enforce
data governance, security, and compliance
best practices.
Mentor junior developers and contribute to team capability building.
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
7+ years of experience in AI/ML, data engineering, or distributed systems.
3+ years of hands-on experience with
Databricks .
Strong expertise in
Apache Spark (PySpark/Scala) .
Advanced programming skills in
Python
(Scala is a plus).
Proven experience in
solution design and architecture .
Deep understanding of
data lakehouse architecture, ETL/ELT pipelines, and data modeling .
Experience with
ML lifecycle management (MLflow, deployment, monitoring) . xsgimln
Experience with cloud platforms ( Azure, AWS, or GCP ).