Principal

Lead MLOps AI Engineer

Key Responsibilities:

1. Data Strategy and Leadership:

- Define and execute the organizations data science strategy, aligning it with the companys overall goals and objectives.

- Provide visionary leadership and mentorship to a team of data scientists, setting high standards for innovation and excellence.

2. Complex Data Analysis:

- Lead and execute complex data analysis and modeling projects, turning data into actionable insights.

- Apply advanced statistical and machine learning techniques to solve challenging business problems.

3. Data Architecture and Infrastructure:

- Collaborate with data engineers and architects to design and implement data pipelines, storage solutions, and data warehousing systems.

- Ensure data infrastructure meets the requirements of data science initiatives.

4. Innovative AI and Machine Learning:

- Drive innovation by exploring emerging AI and machine learning technologies, staying at the forefront of industry advancements.

- Develop and deploy advanced machine learning models for applications such as predictive analytics and recommendation systems.

5. Cross-functional Collaboration:

- Collaborate with stakeholders from various departments, including engineering, product management, and business development, to identify data-driven opportunities and define project objectives.

6. Data Governance and Ethical Practices:

- Establish data governance practices to ensure data quality, security, and compliance with privacy regulations.

- Address ethical considerations and biases in data science projects, ensuring responsible AI development.

7. Research and Development:

- Stay informed about the latest advancements in data science and AI research.

- Contribute to research projects and publish findings in relevant journals or conferences.

8. AI Engineering and MLOPS. Able to create CI/CD pipeline. Hands on with Docker,Container, ML Model Productionalization, Kubernetes cluster , Restful API

9. Able to set up ML operationalization using experiment tracking tool

10. Hands on with Gen AI usecases using RAG architecture

11. Advanced Analytics like NLP, Deep Learning

12. Good at Statistical concepts

Qualifications:

- Ph.D. or Masters in a quantitative field (e.g., Computer Science, Statistics, Mathematics) or equivalent professional experience.

- A strong track record of leadership in data science, including experience leading data science teams and setting data strategy.

- Extensive expertise in data Science, data analysis, machine learning,Deep Learning and AI technologies.

- Proficiency in programming languages such as Python or R.

- Strong problem-solving and critical-thinking skills, with the ability to address complex, unstructured problems.

- CI/CD Pipeline, Container , Docker , Deployment , Restful API, Kubernetes cluster, Jfrog

- Experiment Tracking Tools (Like Mlflow or Mlrun or any)

- Gen AI with RAG architecture

Additional Skills (Preferred but not Mandatory):

- Experience with big data technologies (e.g., Hadoop, Spark).

- Knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud).

- Familiarity with data visualization tools and practices.