AI Principal Engineer - Data Science

Role Overview :

We are seeking a Senior Data Scientist with strong engineering leadership capabilities to drive the adoption and scaling of AI-enabled software development practices across the organization.

This is a hands-on leadership role, combining data science, machine learning, and software engineering, focused on embedding Generative AI and advanced analytics across the Software Development Life Cycle (SDLC).

You will work directly with Scrum teams, engineers, and product stakeholders to design, build, and operationalize AI-driven solutions that improve :

- Developer productivity

- Delivery speed

- Software quality

- Decision-making through data

This is not a research-only or advisory roleyou will actively build, prototype, deploy, coach, and scale AI solutions in production environments.

Key Responsibilities :

- Act as a trusted advisor to the Head of Engineering on AI/ML strategy and adoption

- Define and drive the roadmap for AI-enabled engineering and data-driven decision-making

- Identify high-impact use cases for GenAI, ML, and analytics across engineering workflows

- Translate business and engineering needs into scalable AI solutions

2. Hands-On Data Science & AI Development :

- Design, develop, and deploy machine learning and Generative AI models

- Build solutions such as :

1.Code generation & refactoring assistants

2. AI-driven test generation & automation

3. Intelligent requirement analysis & documentation tools

- Develop RAG pipelines, LLM-powered applications, and AI agents

- Work closely with engineers to integrate models into production systems

3. AI-Enabled SDLC Transformation

- Embed AI across the full SDLC :

1. Requirements understanding (NLP-based analysis)

2. Design recommendations

3. Code generation & optimization

4. Automated testing & defect prediction

5. Code review & quality assurance

- Define AI-driven engineering standards, workflows, and best practices

- Improve software delivery efficiency and quality using data and AI

4. MLOps / AI Ops & Productionization :

- Build and maintain end-to-end ML pipelines

- Ensure robust model deployment, monitoring, and governance

- Implement :

1. Model performance tracking

2. Drift detection

3. Prompt lifecycle management

- Ensure compliance with Responsible AI and security practices

5. Developer Experience & AI Tooling :

- Integrate AI capabilities into :

1. IDEs and developer workflows

2. CI/CD pipelines

3. Testing frameworks

- Improve engineering productivity through :

1. Automation

2. Faster feedback loops

3. Reduced manual effort

- Build reusable AI components, libraries, and internal platforms

6. Data Strategy & Analytics Enablement :

- Enable data-driven insights across engineering and product teams

- Design metrics and dashboards for :

1. Delivery performance (DORA metrics)

2. Code quality

3. Productivity improvements

- Support advanced analytics and experimentation (A/B testing)

7. Community of Practice (CoP) & Enablement

- Establish and lead an AI & Data Science Community of Practice

- Drive adoption through :

1. Workshops, training, and demos

2. Playbooks and reusable frameworks

3. Mentorship and coaching

- Evangelize practical use of AI/ML across teams

8. Measurement & Continuous Improvement :

- Define KPIs for AI impact :

1. Cycle time / lead time

2. Deployment frequency

3. Defect rates

4. Model performance metrics

- Run pilots, measure outcomes, and scale successful solutions

Required Qualifications :

- 7 to 12+ years of experience in Data Science / Machine Learning / AI Engineering

- Proven experience deploying ML models in production environments

- Solid understanding of software engineering and SDLC practices

- Experience working with cross-functional engineering teams

- Strong problem-solving, analytical, and communication skills

- Ability to influence and drive adoption across teams

- Senior Data Scientist / AI Engineering Leader (GenAI, SDLC Transformation, Developer Experience)

Reports To : Head of Engineering