We are looking for a skilled and experienced Software AI Engineer to design, develop, deploy, and maintain scalable Artificial Intelligence and Machine Learning solutions. The ideal candidate will have strong expertise in Python programming, machine learning algorithms, deep learning frameworks, cloud platforms, and production-grade model deployment.
The candidate will collaborate with cross-functional teams to develop intelligent applications, optimize model performance, and integrate AI/ML solutions into existing systems and business workflows.
Requirements
- Design, develop, train, and optimize machine learning and deep learning models for real-world applications.
- Build AI/ML pipelines for data preprocessing, feature engineering, model training, evaluation, and inference.
- Develop AI-powered applications using Python and relevant AI/ML libraries and frameworks.
- Implement Generative AI, Large Language Models (LLMs), NLP, and Retrieval-Augmented Generation (RAG) solutions where required.
- Deploy, monitor, and maintain production-ready AI/ML models on cloud platforms.
- Develop scalable APIs and backend services using FastAPI, Flask, or Django.
- Implement MLOps practices for model versioning, monitoring, testing, and deployment automation.
- Optimize model performance, scalability, reliability, and cloud resource utilization.
- Collaborate with cross-functional teams and provide technical guidance to junior engineers.
Responsibilities:
- Minimum 2+ years of professional experience in AI/ML engineering, machine learning, or a related field.
- Strong proficiency in Python and libraries such as NumPy, Pandas, SciPy, and Scikit-learn.
- Hands-on experience with AI/ML and deep learning frameworks such as PyTorch, TensorFlow, or Keras.
- Strong understanding of machine learning algorithms, deep learning, feature engineering, model evaluation, and hyperparameter tuning.
- Experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (GCP).
- Practical experience deploying AI/ML models into production environments.
- Knowledge of Docker, Kubernetes, CI/CD pipelines, and model-serving technologies.
- Familiarity with MLOps tools such as MLflow or Kubeflow.
- Experience developing REST APIs using FastAPI, Flask, or Django.
- Working knowledge of SQL, databases, Git, automated testing, and monitoring tools.
- Familiarity with Hugging Face Transformers, LangChain, LlamaIndex, LLMs, and RAG is preferred.