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
Must have 8+ years of IT experience
Robust Requirement Elicitation Analysis skills
Should have the capability to understand requirements convert to design
Extensive knowledge on statistical and analytical analysis
Advanced Python, production ML,
Generative AI (LLMs, RAG),
Robust data engineering (SQL/ETL),
cloud (AWS/GCP/Azure),
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

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr Ai Engineer Faridabad (India)
🏢 Ekloud
📍 India