Artificial Intelligence Lead
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Job Location:
Monthly Salary:
Experience Required:
Posted on:
11 hours ago
Vacancies:
1 Vacancy
Job Summary
- Work with LLMs NLP models neural networks and other advanced AI techniques.
- Build scalable data pipelines and automate model training validation and deployment workflows.
- Optimize model performance latency and accuracy using modern techniques.
- Work closely with cross-functional teams including Data Engineering Product and Software Engineering.
- Perform exploratory data analysis feature engineering and experiment tracking.
- Integrate AI models into applications using APIs microservices or cloud-based solutions.
- Stay updated with the latest advancements in AI ML and GenAI technologies.
Requirements
- 10 years of hands-on experience in AI/ML engineering.
- Strong proficiency in Python ML libraries (NumPy Pandas Scikit-learn) and DL frameworks (TensorFlow/PyTorch).
- Experience with LLMs NLP transformers embeddings and prompt optimization.
- Knowledge of MLOps tools like MLflow Kubeflow Airflow or similar.
- Experience in using cloud platforms (AWS / Azure / GCP) for AI model deployment.
- Hands-on experience with vector databases feature stores or model registries (nice to have).
- Familiarity with API development microservices architecture and containerization (Docker Kubernetes).
- Strong understanding of algorithms data structures and model evaluation techniques.
Required Skills:
410 years of hands-on experience in AI/ML engineering. Strong proficiency in Python ML libraries (NumPy Pandas Scikit-learn) and DL frameworks (TensorFlow/PyTorch). Experience with LLMs NLP transformers embeddings and prompt optimization. Knowledge of MLOps tools like MLflow Kubeflow Airflow or similar. Experience in using cloud platforms (AWS / Azure / GCP) for AI model deployment. Hands-on experience with vector databases feature stores or model registries (nice to have). Familiarity with API development microservices architecture and containerization (Docker Kubernetes). Strong understanding of algorithms data structures and model evaluation techniques. RESTful APIs & API design principles Knowledge of GraphQL (preferred) Event-driven architecture (Kafka / RabbitMQ) Advanced knowledge of React ecosystem UI/UX best practices Experience with modern frontend tools (Webpack Vite) SQL (PostgreSQL / MySQL) NoSQL (MongoDB / Redis) Experience with AWS / Azure / GCP CI/CD pipelines (Jenkins GitHub Actions) Docker & Kubernetes System design and scalability Security best practices Performance optimization Version control (Git)
