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The Senior AI & Data Engineer is an individual contributor role that acts as the technical subject matter expert at the intersection of AI engineering and data engineering. This is a uniquely dual-domain role: the successful candidate bridges the organization's data strategy with its AI agenda, ensuring that intelligent systems are built on a foundation of governed, high-quality, well-architected data.
Job Responsibility
Serve as the dual AI & data SME for the team and organization
Define and uphold engineering standards, design patterns, and best practices across both AI and data engineering disciplines
Lead technical discovery for new AI and data use cases
Participate in and lead cross-functional initiatives where AI and data strategy intersect
Mentor and upskill the Applied AI Engineer and AI Data Engineer
Architect and deliver complex agentic AI systems
Design and implement advanced RAG architectures
Lead LLM evaluation frameworks
Assess and implement LLM fine-tuning and alignment strategies
Own LLM integration architecture
Lead the full ML lifecycle
Develop advanced ML solutions across NLP, time-series forecasting, anomaly detection, recommendation, and classification/regression domains
Design and implement MLOps pipelines
Apply statistical rigour
Ensure explainability and fairness assessments
Architect end-to-end AI solutions
Define data contracts and interface specifications
Design for scale, reliability, and cost
Evaluate and recommend AI frameworks, platforms, and tooling
Architect data transformations and ingestion methods for AI products
Define and enforce data engineering standards
Design advanced transformation patterns
Implement automated data quality validation
Champion a data-as-a-product mindset
Provide technical leadership for AI-augmented RPA implementations
Define AI metrics and KPIs for dashboards
Identify automation opportunities
Develop quick PoC's
Continuously evaluate emerging research, models, and frameworks
Present technical findings and recommendations
Contribute to internal AI community of practice
Requirements
Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Engineering, Mathematics, or a related technical discipline
PhD is a plus
7 – 10 years of hands-on experience in AI/ML engineering, applied data science, or LLM engineering roles
Proven track record of delivering production AI systems
Deep expertise with at least two major LLM platforms (Claude, GPT, Gemini, or equivalent)
Significant experience with Collibra or an equivalent enterprise data governance platform
Demonstrated experience leading cross-functional AI initiatives and mentoring junior engineers
Strong ML fundamentals alongside modern generative AI skills
Experience with responsible AI practices, including fairness auditing, explainability, and content safety, is strongly preferred