Senior Data Scientist
First seen by Alion on Sep 22, 2026.
About the job:
Job Description:
Key Responsibilities :
AI Solution Development & Deployment:
- Design, build, test, and deploy end-to-end Machine Learning, AI, and automation solutions tailored to business requirements.
- Generative & Agentic AI Solutions: Implement advanced AI architectures using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic workflows to automate decision-making and enhance user experiences.
- Data Pipeline & MLOps Integration: Collaborate with data and engineering teams to establish data pipelines and apply basic MLOps practices for model deployment, monitoring, and lifecycle management.
- Cloud Infrastructure & AI Services: Deploy and scale ML models on major cloud platforms (Azure, GCP, or AWS) utilizing native AI/ML services and CI/CD pipelines.
- Cross-Functional Collaboration: Partner with software engineers, product managers, and business stakeholders to translate domain requirements into effective technical AI strategies.
- Continuous Innovation: Stay up to date with emerging AI technologies, methodologies, and frameworks to keep our technology stack innovative and competitive.
Required Qualifications & Skills Experience:
- 6+ years of professional experience in Data Science, Machine Learning, Artificial Intelligence, or Intelligent Automation roles. Core Programming: Advanced proficiency in Python and its scientific stack (e.g., NumPy, Pandas, Scikit-Learn, PyTorch, TensorFlow).
- Generative AI & Agentic AI: Strong foundational understanding and hands-on experience with: Large Language Models (LLMs) and Prompt Engineering. Retrieval-Augmented Generation (RAG) architectures and Vector Databases. Agentic AI frameworks and multi-agent workflow automation principles.
- Cloud & Deployment: Experience with cloud-based AI/ML platforms (such as Azure Machine Learning, AWS SageMaker, or GCP Vertex AI) and a practical understanding of model deployment processes (REST APIs, microservices, containerization).
Data & MLOps Fundamentals: Working knowledge of data pipeline orchestration and core MLOps concepts (model tracking, versioning, deployment, and performance monitoring).
- Soft Skills & Core Competencies Problem-Solving: Analytical mindset with strong troubleshooting capabilities and a keen interest in learning and adopting new technologies.
- Communication: Ability to clearly explain complex technical concepts and AI models to non-technical business stakeholders. Teamwork: Collaborative attitude with a proven track record of working effectively in agile, cross-functional teams.
Skills
Data Science, Analytics, Machine Learning, Artificial Intelligence, Data Analytics, Data Scientist, Data Management, Large Language Model