Key Responsibilities
- Design, develop, and deploy AI/ML solutions to solve business challenges and create value from data.
- Build, train, evaluate, and optimize machine learning and generative AI models.
- Develop scalable AI pipelines and integrate AI services into production environments.
- Collaborate with Data Scientists, Data Engineers, Product Managers, and business stakeholders to deliver end-to-end AI solutions.
- Monitor model performance, ensure reliability, and continuously improve AI systems.
- Stay up to date with advancements in AI, machine learning, and generative AI technologies.
Required Qualifications
- 5–6 years of experience in AI Engineering, Machine Learning Engineering, or a related role.
- Strong proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Experience deploying AI/ML models in production environments.
- Solid understanding of machine learning algorithms, deep learning, NLP, and MLOps practices.
- Experience with cloud platforms (AWS, Azure, or GCP) and AI services.
- Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
Nice to Have
- Experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents.
- Knowledge of vector databases and model serving frameworks.
- Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker.
- Exposure to CI/CD pipelines and Infrastructure as Code (IaC).
Soft Skills
- Strong analytical and problem-solving skills.
- Excellent communication and stakeholder management abilities.
- Ability to work independently in a fast-paced environment.
- Curiosity and passion for emerging AI technologies and innovation.