Alquemy's client is a leading organization in the higher education sector. They are seeking an AI Engineer for a One Year Term (contract) Salaried position.
The AI Engineer is responsible for designing, developing, deploying, and monitoring advanced AI and ML models to address business challenges. This role focuses on end-to-end machine learning solutions, including intelligent chatbot applications, MLOps best practices, and scalable AI model deployment on cloud platforms. The successful candidate will work with technologies like AWS SageMaker, Amazon Bedrock, MLflow, Apache Tika, and various MLOps frameworks to ensure robust model versioning, monitoring, and lifecycle management. They will leverage open-source frameworks (e.G., Hugging Face, LangChain, spaCy) and work closely with the Project Manager and AI Data Engineer to develop efficient AI-driven solutions.
Responsibilities:
Design, develop, and implement machine learning models to address specific business needs.
Optimize ML models through rigorous hyperparameter tuning and algorithmic enhancements.
Develop and maintain MLOps pipelines to automate model training, validation, and deployment.
Ensure proper model versioning and reproducibility using MLflow, SageMaker Pipelines, or Kubeflow.
Build and maintain intelligent chatbot solutions, employing techniques such as: Retrieval-Augmented Generation (RAG):
Improve response accuracy with relevant contextual data.
Utilize Apache Tika for extracting and processing unstructured data (PDFs, text, documents, images).
Clean, process, and transform raw datasets for efficient model training and inference.
Deploy ML models on AWS SageMaker, Amazon Bedrock, and other cloud environments.
Requirements:
Bachelor's or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field.
5–8 years of experience in AI/ML development, machine learning engineering, or software engineering roles.
Strong understanding of ML models, fine-tuning, supervised learning, and deep learning techniques.
Hands-on experience with generative AI (GenAI) and agentic AI.
Experience deploying chatbot solutions using RAG, prompt engineering, and knowledge graphs.
Proficiency in MLflow, SageMaker Pipelines, or Kubeflow for model versioning and tracking.
Experience with model monitoring using SageMaker Model Monitor, AWS CloudWatch, or Prometheus.
Strong experience in CI/CD for ML pipelines (GitHub Actions, Jenkins, or AWS CodePipeline). Cloud & Infrastructure:
Hands-on experience with AWS SageMaker, Amazon Bedrock, and Azure OpenAI.
Expertise in deploying AI models via Docker, Kubernetes, and AWS Lambda.
Our client works in a hybrid model with 3-4 days/week required on site. Apply today to learn more!
