XPO
Website:
xpo.com
Job details:
XPO India Shared Services
Position: Lead Data Scientist
Team: XPO India Data Science team
Location: Hyderabad/Pune
Number of Position: 1
Relevant Experience: 7+ years
Position Overview
We are seeking a highly skilled
Lead Data Scientist with deep expertise in
Generative AI, Retrieval-Augmented Generation (RAG), and productionizing advanced machine learning models. The ideal candidate will have strong experience in
MLOps and ML Engineering, ensuring scalable, reliability, and secure deployment of AI solutions into production environments. This role requires both technical leadership and strategic vision to drive impactful AI initiatives across the organization.
Key Responsibilities
AI Solution Development- Design, build, and productionize advanced ML/AI models, including
Generative AI and
RAG-based architectures. - Lead end-to-end model lifecycle: data preparation, feature engineering, training, evaluation, deployment, and monitoring.
MLOps & ML Engineering- Establish and optimize
MLOps pipelines for continuous integration, deployment, and monitoring of ML models. - Implement best practices for
model governance, reproducibility, and scalability. - Collaborate with engineering teams to ensure seamless integration of AI solutions into production systems.
Leadership & Strategy- Provide technical leadership and mentorship to data scientists and ML engineers.
- Partner with product and business stakeholders to translate complex AI concepts into actionable business value.
- Drive innovation by evaluating emerging AI technologies and frameworks.
Operational Excellence- Ensure AI models meet performance, reliability, and compliance standards.
- Develop monitoring frameworks for
model drift, bias detection, and performance degradation. - Champion responsible for AI practices and security-first approaches.
Required Skills & Experience
Technical Expertise- Proven experience in
Generative AI (LLMs, diffusion models, transformers) and
RAG pipelines. - Strong background in
MLOps (CI/CD for ML, model monitoring, orchestration tools like MLflow, Kubeflow, Airflow). - Solid ML Engineering skills:
Python, PyTorch/TensorFlow, distributed training, cloud platforms (AWS, Azure, GCP). - Experience with vector databases (e.g.,
Pinecone, Weaviate, FAISS) and retrieval systems.
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