EAIG:AI Data Engineer

• Design and build data pipelines
• Support feature extraction for LLM workloads
• Enable retrieval and context enrichment
• Ensure data quality, freshness, and lineage

Work Areas & Outcomes –

Data engineering, feature engineering, AI data enablement

Optimal qualification for success on the job is:

• 4-6 years.

For successful execution of the job, the candidate should possess:

Capability & Skill Expectations –

• Hands-on data science skills applied to GenAI use cases, including model fine-tuning, prompt optimization, testing, and evaluation.
• Experience with LLMs, embeddings, retrieval, and agent-based architectures

Platform & Engineering Exposure –

• Experience applying COTS model-development and agent-development platforms to business use cases.
• Ability to integrate APIs, orchestration frameworks, vector databases, and enterprise data sources.

Observability & Evaluation Expectations –

Exposure to observability and evaluation for AI/agent-based apps, including prompt/version tracking, quality evaluation, latency and cost monitoring, hallucination analysis, and human-in-the-loop feedback.