AuxoAI
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 applications.
Location
- Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)
Responsibilities
:
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 practices
Requirements
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
