Senior Machine Learning Engineer

​Company Overview

Giggso, founded in 2017 in Michigan, focuses on helping enterprises successfully navigate digital transformation. As a certified Minority Business Enterprise (MBE), Giggso brings diverse perspectives to solving complex AI and engineering challenges for Fortune 500 clients worldwide. The company specializes in moving organizations from experimental AI pilots to sustainable AI profit centers. Giggso’s core expertise lies at the intersection of Governance, Risk, Compliance (GRC) and Engineering, turning AI governance from a bottleneck into a competitive advantage. Learn more at www.giggso.com.

​

Job Description & Summary

We are seeking a hands-on Senior AI/ML Engineer specializing in MLOps and production pipelines to build, operate, and continuously improve production-grade machine learning and NLP systems. In this role, you will partner with data scientists, product managers, and software engineering teams to standardize the ML lifecycle, ensure compliance-driven model workflows, and enable the rapid, safe delivery of enterprise AI models at scale.

Qualification and Key Skills

- Bachelor's degree in Computer Science, Data Science, Engineering, or a related field with 5+ years of relevant experience; OR a Master's/PhD with 3+ years of relevant experience.

- Proven experience productizing, deploying, and operating ML models or NLP services in live production environments.

- Strong programming skills in Python and extensive experience with core ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).

- Hands-on experience with core NLP techniques, including text classification, entity extraction, and content summarization (e.g., using spaCy, fastText).

-Thorough understanding of the full ML pipeline, starting from data preparation/annotation to model monitoring and automated retraining.

- Strong verbal and written communication skills to negotiate, align, and collaborate effectively within cross-functional teams.

Job Description and Summary

Production ML Platform & Tooling

• ​Design and implement reusable MLOps platform capabilities for training, deployment, and monitoring of ML/NLP systems.
• ​Build standardized end-to-end pipelines for data collection, validation, feature generation, model packaging, and inference.
• ​Own model registry, artifact storage, and metadata lineage to ensure reproducibility and auditability.

​Deployment Engineering & Lifecycle Execution

• ​Deploy models and AI services using containers and orchestration (e.g., Docker, Kubernetes) with robust rollout strategies.
• ​Create automated CI/CD workflows for ML code and text-processing pipelines, including quality gates and validation testing.
• ​Optimize machine learning model training and inference time using caching, batching, and efficient serving patterns for low latency.

​Model Monitoring, Governance & GRC Integration

• ​Implement continuous monitoring for model quality, variations, and data health (detecting drift, bias, performance degradation, and pipeline anomalies).
• ​Build automated feedback loops to trigger investigations, retraining workflows, and safe rollbacks when quality or compliance thresholds are breached.
• ​Support compliance and governance requirements for model usage, data access, and responsible AI practices—turning governance into a competitive engineering advantage.

​Collaboration & Enablement

• ​Partner across data science and business teams to translate data concepts into reliable, scalable ML solutions.
• ​Share technical insights clearly with both technical and non-technical stakeholders to align on executing strategies and improving models.
  

Preferred Qualifications / Skills

• ​Experience with managed cloud platforms (Azure, AWS, or GCP) and containerization frameworks (Kubernetes).
• ​Familiarity with MLOps and orchestration tools such as MLflow, Kubeflow, Airflow, or DVC.
• Certifications in Anthropic Claude

What We Offer:

An exciting opportunity to be part of a growing startup in the cutting-edge field of AI and ML.

A dynamic and inclusive work environment.

Competitive salary with performance-based incentives.

Opportunities for professional growth and development.

Comprehensive benefits package.