AI Developer

Sobre el puesto

Job Responsibilities Years of Experience: 1-3 Yrs Responsibilities: AI & ML Model Development: • Execute PoCs and MVPs for AI and ML projects, focusing on practical implementation. • Develop and refine ML models, including supervised and unsupervised learning algorithms. • Build and deploy GenAI solutions, such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and chatbots. • Collaborate with AI Engineers to integrate models into enterprise applications. Data Handling & Preprocessing: • Collect, clean, and preprocess data to ensure quality inputs for model training. • Conduct exploratory data analysis (EDA) to uncover insights and inform model development. • Work with Data Engineers to ensure smooth data pipelines and infrastructure. Model Evaluation & Optimization: • Evaluate model performance using appropriate metrics and fine-tune algorithms for optimal results. • Monitor GenAI outputs for issues such as bias, hallucinations, and accuracy. • Implement feedback loops for continuous model improvement. Collaboration & Documentation: • Work closely with the AI Lead and cross-functional teams to align projects with business objectives. • Document model development processes, code, and findings for transparency and reproducibility. • Contribute to the development of best practices and playbooks for AI and ML solutions. Continuous Learning & Development: • Stay updated with the latest trends and advancements in AI, ML, and GenAI technologies. • Participate in team knowledge-sharing sessions and training opportunities. Skills/Qualifications: Education: • Bachelors or Masters degree in Computer Science, Data Science, AI, or a related field. Experience: • 1-3 years of experience in data science, machine learning, or AI-related roles. • Hands-on experience with ML model development and deployment. • Exposure to GenAI technologies, including LLMs and chatbot development, is a plus. Technical Skills: • Proficient in Python and SQL for data manipulation and model development. • Familiarity with ML frameworks and libraries such as scikit-learn, TensorFlow, or PyTorch. • Experience with data visualization tools (e.g., Matplotlib, Seaborn) and EDA techniques. • Basic understanding of MLOps tools like MLflow, Airflow, or similar is an advantage. • Exposure to cloud platforms (Azure, AWS, GCP) for AI/ML model deployment is a plus. Soft Skills: • Strong analytical and problem-solving skills. • Eagerness to learn and adapt in a fast-paced, evolving environment. • Detail-oriented with a focus on delivering high-quality work.

Publicada el 9 nov 2026