Responsibilities
- Design, develop, and deploy advanced AI/ML models for real-world applications.
- Lead end-to-end AI solution lifecycle: data collection, preprocessing, modeling, evaluation, and deployment.
- Collaborate with data scientists, software engineers, and product managers to integrate AI into products.
- Implement MLOps best practices for automation, monitoring, and retraining of models.
- Research and evaluate new AI/ML frameworks, tools, and algorithms to enhance solutions.
- Ensure scalability, performance, and reliability of AI systems in production.
- Mentor and guide junior engineers to improve team capabilities.
Basic qualifications
- Education: Bachelor’s or master’s in computer science, AI, Machine Learning, Data Science, or related field.
- Experience: 5+ years in AI/ML engineering with proven project deployment.
- Programming: Strong proficiency in Python; knowledge of R, Java, or C++ is a plus.
- Frameworks & Libraries: TensorFlow, PyTorch, scikit-learn, Hugging Face, OpenAI APIs.
- MLOps & Deployment: CI/CD pipelines, Docker, Kubernetes, cloud platforms (AWS, Azure, GCP).
- Data & Databases: Experience with SQL/NoSQL, data preprocessing, big data tools (Spark, Hadoop).
- APIs & Integration: Experience building REST APIs / microservices for model deployment.
Preferred qualifications
- Strong problem-solving and analytical skills.
- Ability to prioritize tasks and manage time effectively.
- Excellent verbal and written communication skills.
- Strong collaboration and teamwork mindset.
- Demonstrated proactivity, ownership, and commitment to deliver results.
- Interest in continuous learning and professional development.
- Ability to mentor junior engineers and foster knowledge-sharing culture.
Tags & focus areas
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