AI Developer

Machine Learning & Deep Learning Fundamentals

• Strong proficiency in Python with expertise in core ML and deep learning libraries (e.g., scikit-learn, TensorFlow, PyTorch, Keras).
• Deep understanding of classical machine learning algorithms (e.g., regression, classification, clustering) and statistical modeling.
• Solid grasp of deep learning architectures (e.g., CNNs, RNNs, Transformers) and their underlying principles.

Large Language Models (LLMs) & Generative AI

• Proven experience in designing, developing, and deploying Generative AI solutions leveraging LLMs.
• Hands-on experience with prompt engineering, fine-tuning LLMs, and transfer learning for specific downstream tasks.
• Expertise in building and optimizing Retrieval Augmented Generation (RAG) pipelines for enhanced factual accuracy and contextuality.
• Familiarity with prominent LLM APIs (e.g., OpenAI, Google AI, Hugging Face Transformers library).
• Knowledge of techniques for evaluating and mitigating biases in LLMs.

Data Processing & Engineering

• Proficiency in data manipulation and analysis using libraries like Pandas and NumPy.
• Strong understanding of data cleaning, preprocessing, feature engineering, and vectorization techniques.
• Experience with data pipeline development and management, ensuring data quality and readiness for AI models.

Dataset Creation & Management

• Experience in data collection, annotation, and labeling for various data types (text, structured data).
• Ability to implement data augmentation strategies to enrich datasets and improve model generalization.
• Proficiency in establishing robust data splitting methodologies (training, validation, test sets) to ensure unbiased model evaluation.

Model Performance Evaluation & Monitoring

• Expertise in selecting and applying appropriate model evaluation metrics (e.g., Accuracy, Precision, Recall, F1-Score, Confusion Matrix, ROC/AUC for classification etc).
• Familiarity with LLM-specific evaluation techniques such as BLEU, ROUGE, Perplexity, and human evaluation for aspects like faithfulness, relevance, and fluency.
• Experience in model testing methodologies, including offline validation, A/B testing, and adversarial/edge case testing.
• Ability to implement model monitoring systems to detect performance degradation, data drift, and concept drift in production environments.
• Proficiency in setting up alerts and dashboards for continuous model health checks.

AI Application Development & Deployment

• Experience in building end-to-end AI applications, including model serving and API development (e.g., Flask, FastAPI).
• Understanding of MLOps principles for model versioning, monitoring, and deployment.

Problem-Solving & Collaboration

• Excellent analytical and problem-solving abilities in complex data and AI challenges.
• Demonstrated ability to work effectively in a collaborative, agile team environment.

• 2–4 years of relevant experience in machine learning, deep learning, or applied AI roles.
• Strong programming skills in Python, with demonstrated project experience in AI/ML.
• Prior experience in building applications using LLMs or generative AI tools is preferred.
• Familiarity with open-source LLM frameworks and prompt tuning techniques.

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related technical discipline.

• Opportunity to work with a passionate and innovative team.
• Competitive salary and benefits package.
• A collaborative and inclusive work environment.