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.