Data Scientist

About the Role:

As a Lead or Senior Data Scientist at InfoObjects, you will take ownership of designing, developing, and deploying advanced AI and ML models to solve complex business challenges. You will collaborate with cross-functional teams, including data engineers, AI/ML engineers, and business stakeholders, to drive data-driven decision making and innovative solutions.

Key Responsibilities:

• Lead the design and implementation of advanced AI/ML models using frameworks like OpenAI, Hugging Face, PyTorch, or TensorFlow.

• Develop and fine-tune predictive models, including LLMs (GPT, Claude, Llama, Gemini) and generative models (Diffusion Models, GANs), tailored to client and project needs.

• Conduct research on cutting-edge AI techniques, including reinforcement learning (RL), transfer learning, and model optimization. • Collaborate with stakeholders to identify business problems and translate them into AI/ML solutions.

• Lead the integration of AI models into scalable applications and ensure seamless deployment.

• Monitor model performance and implement improvement strategies through continuous training and evaluation.

• Mentor junior data scientists, promoting best practices and ethical AI adoption.

Must-Have Skills:

• Bachelors or Masters degree in Data Science, Computer Science, Machine Learning, or related fields.

• Proficiency in Python, along with experience in frameworks like PyTorch and TensorFlow.

• Strong expertise in LLMs (GPT, Claude, Llama, Gemini), generative models (GANs, Diffusion Models), and model fine-tuning.

• Hands-on experience with APIs and platforms such as OpenAI, Hugging Face, and Vector Databases (Pinecone, Weaviate, ChromaDB). • Vector DBs: Experience with AWS solutions including Aurora Postgres, and Databricks Mosaic AI Vector Search.

• Knowledge of data pipeline orchestration and integration with AWS Knowledge Bases.

• Document Processing: Familiarity with tools like Azure AI Document Intelligence.

• Proficiency in cloud platforms (AWS, GCP, Azure) and experience with Databricks.

• Strong understanding of model evaluation metrics, retrieval-augmented generation (RAG), and ML model lifecycle management. • Proven leadership abilities with a focus on team collaboration and mentoring. • Analytical mindset and strong problem-solving skills.