AI Engineer
Summary
An AI Engineer who designs, develops, and deploys machine learning models for production — building training/evaluation pipelines, monitoring model performance, and integrating AI into applications alongside data scientists and product teams. Core stack is Python with TensorFlow/PyTorch/scikit-learn, cloud platforms, and ML Ops tooling.
Overview:
We are seeking a highly skilled AI Engineer to design, develop, and deploy machine learning and artificial intelligence solutions. The ideal candidate has a strong background in computer science, mathematics, and data science, with hands-on experience building and deploying AI models that solve real-world problems.
Key Responsibilities:
Design, develop, and optimize machine learning and AI models for production use.
Collaborate with data scientists, engineers, and product teams to integrate AI solutions into applications.
Preprocess, clean, and analyze structured and unstructured datasets.
Develop and maintain scalable pipelines for training, evaluation, and deployment of ML models.
Monitor, test, and improve the performance of AI models in production.
Stay updated with the latest AI/ML research, frameworks, and best practices.
Ensure AI systems meet ethical, responsible AI, and security standards.
Qualifications:
Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or related field (PhD is a plus).
Proven experience in machine learning, deep learning, or natural language processing.
Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face, etc.).
Solid understanding of algorithms, data structures, probability, and statistics.
Experience with data handling, feature engineering, and model evaluation.
Familiarity with cloud platforms (AWS, GCP, Azure) and ML Ops tools.
Knowledge of APIs, microservices, and deployment practices.
Nice-to-Have Skills:
Experience with large language models (LLMs), generative AI, or reinforcement learning.
Familiarity with vector databases and semantic search.
Understanding of distributed computing (Spark, Ray, Dask).
Knowledge of CI/CD pipelines for ML (MLflow, Kubeflow, Vertex AI).
Background in computer vision, NLP, or recommendation systems.
Skills
- AI
- API
- AWS
- Azure
- CI/CD
- Cloud
- Computer Vision
- Dask
- Data Science
- Deep Learning
- Distributed Computing
- Feature Engineering
- GCP
- Generative AI
- Hugging Face
- Kubeflow
- LLM
- Machine Learning
- Microservices
- MLflow
- Model Evaluation
- NLP
- Python
- PyTorch
- Recommendation Systems
- Reinforcement Learning
- scikit-learn
- Semantic Search
- Spark
- Statistics
- TensorFlow
- Vector Databases
- Vertex AI