Job
Description
We are looking for a skilled and innovative AI Developer to design, develop, and deploy AI-powe'red applications and services. The ideal candidate will have hands-on experience with machine learning, deep learning, and natural language processing, and will be responsible for building intelligent systems that solve real-world problems. This role requires strong programming skills, a solid understanding of AI/ML algorithms, and the ability to work collaboratively in a fast-paced environment. 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 (eg, 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 (eg, 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 (eg, PostgreSQL, MySQL) NoSQL databases (eg, 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 .
