About Company: A rapidly growing technology solutions company specializing in software development, digital transformation, and innovative product engineering services. Backed by a skilled and passionate team, the organization focuses on delivering scalable and efficient technology solutions that help businesses accelerate growth and improve operational performance. With a strong emphasis on innovation, quality, and timely delivery, the company serves as a trusted technology partner across diverse industries.

Key Responsibilities:

• • Develop and implement machine learning models tailored to business needs.
  • Develop and fine-tune Generative AI models (e.g., LLMs, diffusion models, VAEs) using platforms like Hugging Face, LangChain, or OpenAI).
  • Conduct data collection, cleaning, and pre-processing for model readiness.
  • Train, test, and optimize models to improve accuracy and performance.
  • Work closely with cross-functional teams to deploy AI models in production environments.
  • Perform data exploration, visualization, and feature selection
  • Stay up-to-date with the latest trends in AI/ML and experiment with new approaches.
  • Design and implement Multi-Agent Systems (MAS) for distributed intelligence, autonomous collaboration, or decision-making.
  • Integrate and orchestrate agentic workflows using tools like Agno, CrewAI or LangGraph.
  • Ensure scalability and efficiency of deployed solutions.
  • Monitor model performance and perform necessary updates or retraining.

Requirements:

• • Strong programming skills in Python and experience with libraries like Tensor Flow, PyTorch, Scikit-learn, and Keras.
  • Experience working with vector databases (Pinecone, Weaviate, Chroma) for RAG systems.
  • Good understanding of machine learning concepts, including classification, regression, clustering, and deep learning.
  • Knowledge of knowledge graphs, semantic search, or symbolic reasoning.
  • Proficiency in working with tools such as Pandas, NumPy, and data visualization libraries.
  • Hands-on experience deploying models using REST APIs with frameworks like Flask or FastAPI.
  • Familiarity with cloud platforms (AWS, Google Cloud, or Azure) for ML deployment.
  • Knowledge of version control systems like Git.
  • Experience with Natural Language Processing (NLP), computer vision, or predictive analytics.
  • Exposure to MLOps tools and workflows (e.g., MLflow, Kubeflow, Airflow).
  • Basic familiarity with big data frameworks like Apache Spark or Hadoop.
  • Understanding of data pipelines and ETL processes.