As an AI/ML Engineer III, you will lead the development and operationalization of machine learning models, transforming business challenges into robust, scalable AI solutions. You will define success metrics, design model architectures, and oversee deployment pipelines, ensuring alignment with both technical and organizational goals.
Key Responsibilities
- Translate ambiguous product requirements into clear, measurable machine learning objectives in collaboration with product and data teams
- Design and manage end-to-end ML workflows, including prototyping, training, deployment, and ongoing monitoring
- Build and maintain automated pipelines for continuous integration, validation, and delivery of ML models
- Develop observability tools to track model performance, inference quality, and data drift in production environments
- Architect data strategies that ensure reproducibility, traceability, and high-quality inputs across the ML stack
- Lead the integration of emerging AI technologies, including generative models and large language systems, through proof-of-concept and production rollout
- Collaborate across engineering, infrastructure, legal, and UX teams to establish ethical AI practices and governance frameworks
- Support the development of AI-driven services such as natural language processing for ticket routing and computer vision for asset tracking
- Champion MLOps best practices, including model versioning, monitoring, and secure cloud deployment
Qualifications
You bring 4–6 years of hands-on experience in machine learning implementation and deployment. Your background includes applied work with deep learning, NLP, and cloud-based AI platforms.
- Bachelor’s degree in Computer Science, Data Science, or a related technical field
- Proficiency in Python, SQL, and key ML frameworks including TensorFlow, PyTorch, scikit-learn, and HuggingFace
- Experience with cloud-native AI services such as GCP Vertex AI
- Familiarity with big data tools like Spark and Hadoop
- Strong grasp of model interpretability, bias detection, and ethical AI principles
- Hands-on experience with Git, Docker, Kubernetes, Airflow, MLflow, and REST APIs
- Proven ability to communicate technical concepts to non-technical stakeholders
- Comfort working in Agile environments and across distributed global teams
Technical Environment
The role leverages a modern AI stack including Python, TensorFlow, PyTorch, HuggingFace, and cloud functions, with deployment via Flask/FastAPI and CI/CD pipelines. Workflows are managed using Airflow and monitored through MLflow and custom dashboards.
Work Model
This is a hybrid role with flexibility to support global time zones as needed. Occasional travel may be required for team collaboration or client engagements.
Leadership & Culture
You will serve as a thought leader in responsible AI, guiding cross-organizational alignment on ethics, governance, and innovation. The environment values technical excellence, collaboration, and forward-looking strategy in AI deployment.
