Python AI

Technical Skills:

• Programming Languages: Proficiency in Python.
• Machine Learning & Deep Learning: Experience with frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn.
• NLP & Computer Vision (Optional): Experience with NLTK, SpaCy, OpenCV, Hugging Face Transformers.
• Data Processing & Analysis: Strong knowledge of NumPy, Pandas, Matplotlib, Seaborn for data manipulation and visualization.
• Big Data & Cloud Technologies: Familiarity with Spark, Hadoop, AWS, Google Cloud, Azure ML.
• Model Deployment: Experience with FastAPI, Flask, Docker, Kubernetes, MLflow, TensorFlow Serving.
• Databases: Knowledge of SQL, NoSQL (MongoDB, Firebase), vector databases (Pinecone, FAISS).
• APIs & Integrations: Experience in developing and integrating RESTful APIs, GraphQL.
• DevOps & MLOps: Knowledge of CI/CD pipelines, Jenkins, GitHub Actions, GitLab CI/CD for model deployment and automation.

Problem-Solving & Algorithm Development:

• Strong understanding of data structures, algorithms, probability, and statistics.
• Experience with optimization techniques for model efficiency and scalability.

Software Development Best Practices:

• Experience with code versioning tools like Git, GitHub, GitLab, Bitbucket.
• Ability to write efficient, modular, and reusable code following best practices.

Nice to have skills:

• Experience with LLMs (Large Language Models) and Generative AI (e.g., OpenAI API, LangChain).
• Experience with Edge AI & IoT applications.
• Familiarity with AutoML and model optimization techniques (ONNX, TensorRT).
• Knowledge of Reinforcement Learning (RL) algorithms.
•  C++, Java, or R as a plus.

What will you do?

• Design, develop, and train machine learning and deep learning models.
• Implement AI solutions using Python and frameworks like TensorFlow, PyTorch, Keras, Scikit-learn.
• Optimize model performance, accuracy, and scalab
• Collect, clean, and preprocess structured and unstructured data.
• Perform feature engineering to improve model performance.
• Work with large datasets and big data tools (e.g., Spark, Hadoop).
• Deploy AI models using FastAPI, Flask, TensorFlow Serving, or Docker.
• Implement CI/CD pipelines for automated model training and deployment.
• Monitor and maintain production AI systems, ensuring model accuracy and performance.
• Stay updated with the latest AI/ML advancements and best practices.
• Experiment with NLP (Hugging Face, Transformers, GPT models) and Computer Vision (OpenCV, YOLO, Detectron).
• Explore model optimization techniques (e.g., ONNX, TensorRT).
• Develop and integrate AI-powered APIs with web and mobile applications.
• Work with cloud platforms (AWS, GCP, Azure) for scalable AI solutions.
• Implement serverless architectures for AI applications.
• Work closely with data scientists, engineers, and product teams to define AI-driven solutions.
• Document AI workflows, experiments, and performance metrics.
• Participate in code reviews, knowledge sharing, and technical discussions.

Conditions:

• Long term project
• Challenging tasks
• Remote friendly
• Competitive salary based on experience
• No micromanagement