Senior Data Scientist
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Job Summary
- End-to-End Development: Design build and productionize large-scale machine learning generative AI and agentic systems and models.
- Operational Excellence: Implement robust MLOps workflows using MLflow to ensure model reliability scalability and performance tracking.
- Production Deployment: Take ownership of the full lifecycle of AI products ensuring models move successfully from experimental stages to live production environments.
- Experience: 4 years of hands-on experience in Data Science Machine Learning GenAI and MLOps.
- Track Record: Proven experience productionizing models (must have moved models beyond the sandbox/prototype phase).
- Required: Expert-level proficiency with MLflow and model deployment and monitoring on Databricks.
- Required: Experience with Databricks Spark Lakebase Vector Search.
- Required: Experience with at least one agent development framework like Langchain Langgraph DSPy etc.
- Good to have: Hands on experience with Databricks Apps AI/BI Dashboards Genie etc.
- Good to have: Databricks Asset Bundle (DAB) Git experience with large repo management (GitHub ADO etc.)
Required Skills:
- Bachelors degree in Computer Science Engineering or a related field. - Strong proficiency in Python programming language and its associated frameworks (e.g. Django Flask). - Experience in front-end technologies such as HTML CSS JavaScript and modern JavaScript frameworks (e.g. React Angular ). - Solid understanding of web technologies including HTTP RESTful APIs and web security. - Proficiency in database design and development using SQL and familiarity with ORMs (). - Familiarity with version control systems (e.g. Git) and collaborative development workflows. - Knowledge of software engineering principles design patterns and best practices. - Experience with cloud platforms (e.g. AWS Azure) and deployment of web applications. - Strong problem-solving skills and attention to detail. - Excellent communication and collaboration abilities. - Ability to work effectively in a fast-paced and dynamic environment. Good to have Qualifications: - Experience in building scalable and distributed systems. - Familiarity with containerization and orchestration technologies (e.g. Docker Kubernetes). - Knowledge of DevOps practices and continuous integration/continuous deployment (CI/CD) pipelines. - Experience with Agile development methodologies.
