GCP AI Engineer

What You'll Do

• Own end-to-end delivery of production AI/ML solutions, from problem framing and data analysis through modeling, deployment, monitoring, and retraining.
• Build, train, and validate models (supervised, unsupervised, NLP, retrieval, document intelligence, computer vision, GenAI) using approved data and platforms.
• Establish rigorous evaluation, explainability, bias assessment, and model governance practices suited to a regulated environment.
• Implement MLOps for versioning, CI/CD, deployment, observability, and drift detection.
• Develop secure APIs and integration patterns, partnering with architecture, engineering, data, risk, and cybersecurity teams.
• Take prototypes to scalable production and produce clear documentation, runbooks, and knowledge-transfer materials.
• Mentor developers through design reviews, pairing, and workshops.

Required Qualifications

• Bachelor's or advanced degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• 12+ years in software engineering, data science, or ML, including at least one complete AI/ML lifecycle delivered to production.
• Strong foundation in statistics, probability, linear algebra, optimization, and model evaluation.
• Tech stack will be a combo of onprem tooling like dbs, confluence, sharepoint, and GCP services
• Hands-on model development in Python with standard ML libraries, plus experience with NLP, embeddings, RAG, LLMs, VLMs, or document intelligence.
• Solid grasp of data preparation, feature engineering, class imbalance, leakage prevention, and tuning.
• Experience with MLOps, containerization, model registries, and secure REST APIs and microservices.
• Ability to work within financial-services constraints around privacy, model risk, explainability, and auditability.
• Proven mentoring ability and strong communication with technical and non-technical audiences in Agile, distributed teams.