Archived listing. The details below describe a past opening.
Required Skills & Experience
- Senior-level hands-on machine learning and/or AI engineering experience
- Strong Google Cloud Platform (GCP) experience
- Hands-on experience with Google's ML/AI ecosystem
- Gemini and/or Google Generative AI experience
- Strong Python development skills
- Experience developing and deploying ML, Generative AI, and/or LLM solutions into production
- Experience operationalizing ML/AI workloads using MLOps practices
- Understanding of data pipelines and how enterprise data feeds ML/AI applications
- Ability to write, review, troubleshoot, and deploy production code using GitHub-based development workflows
- Ability to operate at a Senior Engineer / Architect level and communicate technical solutions to customers
Nice to Have Skills & Experience
- Vertex AI
- Gemini Enterprise
- Agentic AI
- Retrieval-Augmented Generation (RAG)
- PyTorch and/or TensorFlow
- BigQuery
- Dataflow / Apache Beam
- Dataproc / Spark / PySpark
- MLOps tooling and model monitoring
- GCP Professional Machine Learning Engineer certification
- GCP Professional Cloud Architect certification
- Experience supporting federal/public-sector or highly regulated customers
- Experience with secure or classified cloud environments
- Active security clearance
- Ability to span both ML/AI engineering and GCP data engineering
Job Description
We are seeking a Senior GCP ML/AI Engineer to design, build, deploy, and operationalize production AI and machine learning solutions using Google Cloud technologies. The role is focused on hands-on engineering with Google's ML/AI ecosystem, including Gemini and related GCP AI services.
The engineer will develop production ML, Generative AI, and/or LLM solutions in Python and integrate those solutions with enterprise data pipelines. Responsibilities include model and LLM pipeline development, deployment, MLOps, production integration, performance optimization, and technical architecture.
This is a hands-on engineering position. The engineer must be able to develop Python code, review code in GitHub, troubleshoot implementations, and contribute directly to production delivery while also operating at a senior technical level.
The role is primarily remote but requires the ability to travel to the Reston, VA/customer location periodically. On-site requirements may range from several days to approximately 1–2 weeks in a month depending on customer needs.