Lead Engineer – AI/ML & Cloud Solutions
Role Overview
Drive the development and implementation of artificial intelligence and machine learning systems within a cloud-first architecture. This role is responsible for designing end-to-end automation frameworks, building robust ML models, and deploying AI-powered applications at scale using Google Cloud Platform (GCP). You will lead technical direction, guide engineering teams, and shape the organization’s AI strategy through hands-on development and architectural leadership.
Key Responsibilities
- Design and implement machine learning pipelines for predictive modeling, text classification, and log analysis applications
- Develop and deploy AI components aligned with business objectives using Python, Java, and open-source tools
- Build and maintain scalable cloud-based solutions using Compute Engine, Cloud Functions, App Engine, and Cloud Run
- Implement MLOps practices including CI/CD, model monitoring, and alerting systems
- Create containerized services using Docker and orchestrate deployments in cloud environments
- Lead technical design discussions and mentor junior and mid-level engineers in cloud architecture and GCP best practices
- Train, evaluate, and retrain machine learning models to ensure performance and accuracy
- Develop NLP and case management solutions using Pega platforms and explore alternative open-source technologies
- Collaborate with engineering and leadership teams on prototyping, testing, and deployment of AI systems
- Advise on strategic decisions related to AI policy, technology adoption, and long-term innovation
- Ensure compliance with design, security, and functional requirements throughout the development lifecycle
Required Qualifications
- Strong coding skills in Python and SQL with proven experience in ML frameworks such as TensorFlow, PyTorch, and scikit-learn
- Deep knowledge of REST API development, NoSQL and RDBMS design, and data optimization techniques
- Hands-on experience with GCP services including Compute Engine, Cloud Functions, and Cloud Run
- Proficiency in Infrastructure as Code using Terraform or CloudFormation
- Experience analyzing structured and unstructured data using NLP, text mining, and classification algorithms
- Solid understanding of object-oriented programming, functional design, and software architecture principles
- Familiarity with OpenCV, OCR, CNN, LSTM, and MLlib for image and text processing
- Ability to model data effectively and visualize insights from complex datasets
- Experience with agile development, iterative delivery, and production deployments in high-volume systems
- Comfortable working in dynamic, fast-moving environments with strong collaboration skills
Preferred Qualifications
- Professional certification in AI/ML or Google Cloud Platform
- Background in microservices, API integration, and public cloud deployment
- Knowledge of DevOps practices and security compliance in cloud environments
- Experience with PRPC or Pega Robotics platforms
