EAIG:AI Tech Engineer

• Develop AI services and APIs
• Integrate COTS AI/agent platforms
• Support deployment pipelines
• Optimize performance and cost
• Ensure reliability of AI services in production

Work Areas & Outcomes –

Platform engineering, AI services, MLOps

Optimal qualification for success on the job is:

• 5-8 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.