Lead Data Scientist & AI Engineer
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Job Location:
Monthly Salary:
Posted on:
2 days ago
Vacancies:
1 Vacancy
Job Summary
Role Summary
The Lead Data Scientist & AI Engineer is responsible for designing building and scaling data science and AI solutionsparticularly Generative AI and LLM-powered systemsthat 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 leadersetting 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.
Required Experience:
IC
Key Skills
- Apache Hive
- S3
- Hadoop
- Redshift
- Spark
- AWS
- Apache Pig
- NoSQL
- Big Data
- Data Warehouse
- Kafka
- Scala
About Company
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