ShyftLabs is seeking to design, build, and maintain scalable ML infrastructure and lead initiatives in AI-driven solutions, natural language processing (NLP), and chatbot development to drive measurable business impact for Fortune 500 clients
Requirements
- Hands-on experience with AWS services including SageMaker, EC2, S3, Lambda, Glue, and other ML-focused AWS offerings
- Proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch, scikit-learn)
- Experience with NLP frameworks and libraries (spaCy, Hugging Face Transformers, Rasa, OpenAI APIs, or similar)
- Experience designing, building, and deploying chatbots or conversational AI systems at scale
- Knowledge of orchestration tools (Apache Airflow, Kubeflow, or MLflow)
- Familiarity with CI/CD pipelines and DevOps tools for continuous integration and deployment
- Experience with containerization and orchestration (Docker, Kubernetes)
Responsibilities
- Design and implement conversational AI platforms, intelligent chatbots, and NLP-driven solutions to enhance customer engagement and automate business processes
- Design, build, and maintain highly scalable, robust, and efficient cloud infrastructure using AWS services (SageMaker, EC2, S3, Lambda, and other ML-focused AWS offerings)
- Develop automation and orchestration of ML pipelines, integrating data ingestion, feature engineering, model training, and deployment processes
- Build and deploy production-ready ML models for applications including pricing optimization, operational efficiency, predictive analytics, and conversational AI
- Implement NLP solutions for tasks such as intent recognition, entity extraction, sentiment analysis, and contextual understanding
- Optimize data processing pipelines and AWS resources to ensure low-latency, cost-effective operation
- Implement monitoring, alerting, and failover strategies to ensure platform reliability and model performance
Other
- Bachelor’s or Master’s degree in Computer Science, Engineering, Machine Learning, or a related quantitative field
- 3+ years of experience in machine learning engineering with a focus on ML infrastructure and AI applications
- Strong understanding of ML algorithms, model evaluation, and production deployment challenges
- Ability to work in a hybrid environment with 3+ days per week spent in the downtown Atlanta office
- Commitment to creating a safe, diverse, and inclusive environment
