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The AI Developer designs, develops, deploys, and monitors 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.
Key responsibilities include:
AI Model Development & Engineering: Design and implement machine learning models, optimize them, and develop MLOps pipelines for automation, versioning, and reproducibility.
Chatbot Development & Advanced AI Techniques: Build and maintain intelligent chatbot solutions using techniques like Retrieval-Augmented Generation (RAG), Prompt Engineering, and Knowledge Graph Integration.
Data Processing & Feature Engineering: Utilize tools like Apache Tika for extracting and processing unstructured data, cleaning datasets, and implementing feature extraction.
Model Deployment & Monitoring: Deploy ML models on cloud platforms (AWS SageMaker, Amazon Bedrock), implement CI/CD pipelines, and monitor model performance post-deployment.
Cloud & MLOps Integration: Build scalable AI solutions on AWS, OpenAI, and Azure OpenAI, utilizing Docker, Kubernetes, and microservices architecture, while optimizing cloud costs.
Collaboration & Documentation: Work with cross-functional teams, document model architectures, and conduct code reviews.
Continuous Improvement & Innovation: Automate MLOps tasks and stay updated with the latest AI, ML, and cloud computing trends.
This position reports to the Sr. Manager, Digital Experience and Creative Media, and provides senior-level software development to our clients. The role involves ensuring the reliability and high availability of mission-critical systems.
Qualifications:
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.
Strong understanding of ML models, fine-tuning, supervised learning, and deep learning techniques.
Hands-on experience with generative AI (GenAI) and agentic AI.
Proficiency in MLOps tools like MLflow, SageMaker Pipelines, or Kubeflow, and experience with model monitoring and CI/CD for ML pipelines.
Hands-on experience with AWS SageMaker, Amazon Bedrock, Azure OpenAI, Docker, Kubernetes, and AWS Lambda for AI model deployment.
Experience with Apache Tika for document processing, data preprocessing, feature engineering, and familiarity with vector databases.
Strong programming skills in Python (preferred), Java, or C++, and proficiency in ML libraries/frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn.
Familiarity with Hugging Face, LangChain, spaCy, and other AI toolkits.
Strong analytical, problem-solving, and communication skills, with the ability to work independently and collaboratively.
Preferred: Experience integrating AI with enterprise platforms like ServiceNow, familiarity with additional open-source platforms, and a background in NLP, document AI, and multi-modal AI applications.
