Responsibilities:

• Develop and implement machine learning models and algorithms for classification, regression, clustering, recommendation, and more.
• Build and maintain data pipelines for training and inference workflows.
• Collaborate with data scientists, product managers, and software engineers to integrate AI models into production systems.
• Optimize model performance and scalability for real-time and batch processing.
• Conduct experiments, evaluate model performance, and iterate based on results.
• Stay up to date with the latest research and advancements in AI/ML and apply them to practical use cases.
• Document code, processes, and model behavior for reproducibility and compliance.

Basic Requirements:

1. Programming Languages

• Python: Core language for AI/ML development. Proficiency in libraries like:
• NumPy, Pandas for data manipulation
• Matplotlib, Seaborn, Plotly for data visualization
• Scikit-learn for classical ML algorithms
• Familiarity with R, Java, or C++ is a plus, especially for performance-critical applications.

2. Machine Learning & Deep Learning Frameworks

Experience building models using the following:

• TensorFlow and Keras for deep learning
• PyTorch for research-grade and production-ready models
• XGBoost, LightGBM, or CatBoost for gradient boosting
• Understanding of model training, validation, hyperparameter tuning, and evaluation metrics (e.g., ROC-AUC, F1-score, precision/recall).

3. Natural Language Processing (NLP)

Familiarity with:

• Text preprocessing (tokenization, stemming, lemmatization)
• Vectorization techniques (TF-IDF, Word2Vec, GloVe)
• Transformer-based models (BERT, GPT, T5) using Hugging Face Transformers
• Experience with text classification, named entity recognition (NER), question answering, or chatbot development.

4. Computer Vision (CV)

Experience with:

• Image classification, object detection, segmentation
• Libraries like OpenCV, Pillow, and Albumentations
• Pretrained models (e.g., ResNet, YOLO, EfficientNet) and transfer learning

5. Data Engineering & Pipelines

• Ability to build and manage data ingestion and preprocessing pipelines.
• Tools: Apache Airflow, Luigi, Pandas, Dask
• Experience with structured (CSV, SQL) and unstructured (text, images, audio) data.

6. Model Deployment & MLOps

Experience deploying models as:

• REST APIs using Flask, FastAPI, or Django
• Batch jobs or real-time inference services
• Familiarity with:
• Docker for containerization
• Kubernetes for orchestration
• MLflow, Kubeflow, or SageMaker for model tracking and lifecycle management

7. Cloud Platforms

• Hands-on experience with at least one cloud provider:
• AWS (S3, EC2, SageMaker, Lambda)
• Google Cloud (Vertex AI, BigQuery, Cloud Functions)
• Azure (Machine Learning Studio, Blob Storage)
• Understanding of cloud storage, compute services, and cost optimization.

8. Databases & Data Access

Proficiency in:

• SQL for querying relational databases (e.g., PostgreSQL, MySQL)
• NoSQL databases (e.g., MongoDB, Cassandra)
• Big data tools like Apache Spark, Hadoop, or Databricks is a plus

9. Version Control & Collaboration

• Experience with Git and platforms like GitHub, GitLab, or Bitbucket.
• Familiarity with Agile/Scrum methodologies and tools like JIRA, Trello, or Asana.

10. Testing & Debugging

• Writing unit tests and integration tests for ML code.
• Using tools like pytest, unittest, and debuggers to ensure code reliability.