AuxoAI is hiring a 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 applications.
Responsibilities
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Own the full ML lifecycle: model design, training, evaluation, deployment.
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Design production-ready ML pipelines with CI/CD, testing, monitoring, and drift detection.
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Fine-tune LLMs and implement retrieval-augmented generation (RAG) pipelines.
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Build agentic workflows for reasoning, planning, and decision-making.
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Develop both real-time and batch inference systems using Docker, Kubernetes, and Spark.
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Leverage state-of-the-art architectures: transformers, diffusion models, RLHF, and multimodal pipelines.
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Collaborate with product and engineering teams to integrate AI models into business applications.
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Mentor junior team members and promote MLOps, scalable architecture, and responsible AI best practices.
Requirements
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2+ years of experience in designing, deploying, and scaling ML/DL systems in production.
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Proficient in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.
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Experience with LLM fine-tuning, LoRA/QLoRA, vector search (Weaviate/PGVector), and RAG pipelines.
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Familiarity with agent-based development (e. g., ReAct agents, function-calling, orchestration).
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Solid understanding of MLOps: Docker, Kubernetes, Spark, model registries, and deployment workflows.
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Strong software engineering background with experience in testing, version control, and APIs.
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Proven ability to balance innovation with scalable deployment.
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B. S. /M. S. /Ph. D. in Computer Science, Data Science, or a related field.
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Bonus: Open-source contributions, GenAI research, or applied systems at scale.
This job was posted by Anupam Arya from AuxoAI.