Sr AI engineer

Section II - Job Evaluation Topics and Weight (Questions and evaluation are based on below)

Topic of Evaluation / Skills

Mandatory / Non-mandatory

Percentage

(Skills / Topics should sum up to 100%)

(Enter only multiples of 5)

GeGenerative AI (LLMs, RAG)

Mandatory

40%

Model evaluation responsible AI

Mandatory

40%

Data Engineering-10%

Mandatory

10%

Productionize ML pipelines

Mandatory

10%

Job Requirements

1. Must have 8+ years of IT experience
2. Strong Requirement Elicitation Analysis skills
3. Should have the capability to understand requirements convert to design
4. Extensive knowledge on statistical and analytical analysis
5. Advanced Python, production ML,
6. Generative AI (LLMs, RAG),
7. Strong data engineering (SQL/ETL),
8. cloud (AWS/GCP/Azure),
9. MLOps (CI/CD, Docker/K8s, MLflow),

Model evaluation responsible AI.

Key Responsibilities

Build and scale GenAI solutions (RAG/agents),.

Productionize ML pipelines, ship Python services,

Partner with data/infra and client stakeholders

Section V - Sample Questions for Training Model

• what are Agents and Agentic AI. Difference btw them
• Is it possible to build an Agent without LLM models, justify
• what are all different types of prompting techniques
• if we have any PII data, should we pass the data to the LLM model, justify with examples
• Any one python Hands-on question
• Does naive RAG increase hallucination justify
• How do you evaluate RAG systems end-to-end
• What is embedding models.
• What are multi-model LLMs
• Can we replace all NLP models with LLM models justify

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