AI/ML Engineer
Key Responsibilities
- Develop and train machine learning and deep learning models.
- Build data pipelines for data collection, cleaning, and feature engineering.
- Evaluate, optimize, and fine-tune model performance.
- Deploy AI/ML models into production environments.
- Monitor model accuracy, latency, and reliability after deployment.
- Implement MLOps practices for automated training, testing, and deployment.
- Collaborate with data scientists, software engineers, and product teams.
- Conduct experiments and A/B testing to improve model performance.
- Document models, workflows, and technical solutions.
Required Technical Skills
- Programming: Python, Java, C++, or Scala
- Machine Learning: Regression, Classification, Clustering, Recommendation Systems
- Deep Learning: Neural Networks, CNNs, RNNs, Transformers, LLMs
- Frameworks: TensorFlow, PyTorch, Scikit-learn
- Databases: SQL, NoSQL
- Cloud Platforms: AWS, Azure, GCP
- MLOps Tools: MLflow, Kubeflow, Docker, Kubernetes
- Big Data: Spark, Hadoop
- Version Control: Git
- API Development: FastAPI, Flask, REST APIs
Preferred Skills
- Generative AI and LLMs (GPT, Claude, Llama)
- Prompt Engineering and RAG systems
- NLP, Computer Vision, or Recommendation Systems
- Distributed computing and scalable ML infrastructure
- Strong communication and problem-solving skills
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