Senior Data Scientist

5 days ago

Hyderabad, India Rivet HR Full time

Role & Responsibilities : Our client is hiring a Senior Data Scientist with strong expertise in AI, machine learning engineering (MLE), and generative AI. You will play a leading role in designing, deploying, and scaling production-grade ML systems including large language model (LLM)-based pipelines, AI copilots, and agentic workflows. This role is ideal for someone who thrives on balancing cutting-edge research with production rigor and loves mentoring while building impact-first AI : - Own the full ML lifecycle : model design, training, evaluation, 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 both real-time and batch inference systems using Docker, Kubernetes, and Spark- Leverage state-of-the-art architectures : 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 promote MLOps, scalable architecture, and responsible AI best practicesIdeal Candidate : - 5+ years of experience in designing, deploying, and scaling ML/DL systems in production- Proficient 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 : 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 : Open-source contributions, GenAI research, or applied systems at scale (ref:hirist.tech)

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