Sr. Data Scientist

As a Senior Data Scientist at our client's company, you will be responsible for leading the design, deployment, and scaling of production-grade ML systems with a focus on AI, machine learning engineering (MLE), and generative AI. Your role will involve working on large language model (LLM)-based pipelines, AI copilots, and agentic workflows. You will be expected to balance cutting-edge research with production rigor and mentor team members while building impact-first AI applications. **Responsibilities:** - Own the full ML lifecycle, including model design, training, evaluation, and deployment - Design production-ready ML pipelines with CI/CD, testing, monitoring, and drift detection - Fine-tune LLMs and implement retrieval-augmented generation (RAG) pipelines - Build agentic workflows for reasoning, planning, and decision-making - Develop real-time and batch inference systems using Docker, Kubernetes, and Spark - Utilize state-of-the-art architectures like transformers, diffusion models, RLHF, and multimodal pipelines - Collaborate with product and engineering teams to integrate AI models into business applications - Mentor junior team members and advocate for MLOps, scalable architecture, and responsible AI best practices **Ideal Candidate:** - Must have 5+ years of experience in designing, deploying, and scaling ML/DL systems in production - Proficiency in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX - Experience with LLM fine-tuning, LoRA/QLoRA, vector search (Weaviate/PGVector), and RAG pipelines - Familiarity with agent-based development (e.g., ReAct agents, function-calling, orchestration) - Solid understanding of MLOps tools like Docker, Kubernetes, Spark, model registries, and deployment workflows - Strong software engineering background with experience in testing, version control, and APIs - Proven ability to balance innovation with scalable deployment - B.S./M.S./Ph.D. in Computer Science, Data Science, or a related field - Bonus points for open-source contributions, GenAI research, or experience in applied systems at scale,