Lead Data Scientist & AI Engineer

Role Summary

The Lead Data Scientist & AI Engineer is responsible for designing, building, and scaling data science and AI solutions—particularly Generative AI and LLM-powered systems—that deliver measurable business and customer impact. This role owns end-to-end AI initiatives, from problem framing and model design to production deployment, monitoring, and continuous improvement.

The role serves as a technical authority for AI and GenAI, defining best practices, ensuring reliability and governance, and mentoring team members, while working closely with Product, Engineering, and Business stakeholders.

Key Responsibilities

1. AI, GenAI & Technical Leadership

• Lead the design and implementation of machine learning, Generative AI, and LLM-based solutions aligned with product and business objectives.

• Translate ambiguous business and product problems into well-defined AI problem statements, success metrics, and evaluation frameworks.

• Act as the technical decision-maker for model selection, system architecture, prompt strategy, retrieval design, and deployment approach.

2. GenAI / LLM System Development

• Design and build LLM-powered systems such as AI agents, copilots, document intelligence, decision support tools, and natural language interfaces.

• Implement advanced GenAI patterns including Retrieval-Augmented Generation (RAG), agentic systems, tool/function calling, prompt chaining, and hybrid rule-based + LLM systems.

• Define and continuously improve GenAI evaluation metrics beyond accuracy, including relevance, hallucination rate, latency, cost, and user adoption.

3. End-to-End AI Delivery & Productionization

• Own the full AI lifecycle: data exploration, feature engineering, model training or fine-tuning, evaluation, deployment, monitoring, and retraining.

• Ensure AI models and GenAI systems are scalable, reliable, secure, and cost-efficient in production environments.

• Collaborate with Data Engineering and Platform teams to integrate AI solutions into products and internal systems.

4. Governance, Reliability & Responsible AI

• Establish best practices for experimentation, versioning, monitoring, and quality control for both ML and GenAI systems.

• Ensure responsible AI usage, including data privacy, PII protection, access control, explainability, and risk mitigation.

• Monitor production AI systems and lead incident analysis, remediation, and post-mortems related to AI reliability or model degradation.

5. Team, Mentorship & Stakeholder Management

• Mentor and guide Data Scientists and AI Engineers, raising technical standards and delivery quality.

• Review model designs, GenAI architectures, prompts, and code to ensure robustness and maintainability.

• Communicate complex AI and GenAI concepts clearly to non-technical stakeholders, influencing product and strategic decisions.

Requirements

• 5+ years of experience in Data Science, Machine Learning, or AI Engineering, with demonstrated ownership of production-grade AI systems.

• Strong hands-on experience building, deploying, and operating Generative AI and LLM-based systems in customer-facing real-world applications.

• Deep understanding of machine learning fundamentals, statistics, model evaluation, and trade-offs between different modeling approaches.

• Practical expertise in GenAI patterns such as prompt engineering, RAG, LLM orchestration, agent-based systems, and hybrid LLM + rule-based architectures.

• Proficiency in Python and SQL, with experience writing production-quality, maintainable code.

• Experience integrating AI systems with data pipelines, APIs, analytical data warehouses, and cloud-based infrastructure.

• Familiarity with model deployment patterns (real-time, batch, async), AI monitoring, cost control, and performance optimization.

• Strong problem-framing and structured thinking skills when dealing with ambiguous business or product requirements.

• Proven ability to act as a technical leader—setting standards, mentoring others, and influencing cross-functional teams without relying solely on formal authority.

• Solid communication skills, with the ability to explain complex AI and GenAI concepts to product, engineering, and business stakeholders.

• Experience with AI governance, data privacy, and responsible AI practices in production environments is strongly preferred.