GEN AI

Key Responsibilities
Build and fine-tune Generative AI models (LLMs, diffusion models, etc.) for various applications.
Work with agent and multi-agent frameworks to build task-specific or collaborative AI systems.
Develop and deploy ML pipelines for training, inference, and evaluation.
Collaborate with cross-functional teams (Product, Data Engineering, DevOps) to integrate ML models into products.
Conduct data preprocessing, exploratory analysis, and feature engineering.
Stay updated with state-of-the-art research in ML/GenAI and apply it to practical problems.
Optimize models for performance, scalability, and efficiency.
Work with APIs like OpenAI, AZURE OpenAI, and others for rapid prototyping and deployment.
Contribute to internal tools and frameworks to support ML experimentation and monitoring.
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Required Skills & Qualifications
Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or related field.
3 to 5 years of hands-on experience in Machine Learning and/or NLP projects.
Proficiency in Python and popular ML libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers).
Practical experience with agent and/or multi-agent frameworks (e.g., LangGraph, CrewAI, AutoGen, AutoGPT, BabyAGI, etc.) is highly desirable.
Experience working with LLMs (GPT, Claude, etc.).
Familiarity with prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning techniques.
Strong understanding of data structures, algorithms, and ML concepts.
Experience in deploying models using tools like Docker, FastAPI, Flask, or MLflow.
Knowledge of cloud platforms (AWS, GCP, or Azure) is a plus.

Experience with vector databases (e.g., PG Vector, Pinecone, Weaviate).
Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Airflow).
Publications or contributions to open-source projects in ML/GenAI.
Familiarity with ethical AI principles and responsible AI practices