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Generative AI Engineer

Nagarro — Oman · Posted ~4 days ago

Junior Full-time

Skills

Python RAG LLM APIs Prompt Engineering Model Deployment Docker LLMs FAISS Chroma Pinecone

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Summary ✨ AI‑Generated

Seeking an AI engineer to implement generative AI applications using LLMs, retrieval workflows, and deployment technologies. The role offers practical experience developing intelligent solutions under guidance.

Highlights

Early-career opportunity to gain hands-on experience building and deploying generative AI solutions with modern machine learning workflows.

Description

Job Description Must have Skills : Python (Strong), Model Packaging & Deployment (Strong), RAG Workflow (Strong), Prompt Engineering with LLMs (Strong) Good To Have Skills : Vector Databases and Embeddings (Capable) Job description: Generative AI developer (experience 1 to 2 years) Early-career AI/ML Engineer supporting the development and deployment of Generative AI solutions (LLMs, RAG systems). Focus on hands-on implementation, integration, and learning-by-delivery, under guidance. Must Have: 1. Strong foundation in Python (data handling, APIs, scripts) 2. Understanding of APIs, Docker, or deployment workflows 3. Exposure to deploying ML/AI models (even in projects/internships) 4. Understanding of embeddings, retrieval flow 5. Hands-on exposure through projects 6. Experience working with GPT/Llama APIs 7. Ability to structure prompts and evaluate outputs 8. Vector Databases (Exposure Level) - Familiarity with FAISS / Chroma / Pinecone and basic usage in projects. 9. . ML Fundamentals: Core concepts - overfitting, evaluation metrics, basic algorithms. Ability to reason about model behavior. Core Responsibilities (Execution Under Guidance) 1. Assist in building RAG-based GenAI solutions for enterprise use cases. 2. Develop Python-based services/APIs integrating LLMs. 3. Support data preprocessing, embeddings, and retrieval pipelines. 4. Contribute to model deployment and integration tasks. 5. Debug and improve existing pipelines under supervision. 6. Work closely with senior engineers to understand production constraints (latency, cost, accuracy) Good to Have 1. LangChain / LlamaIndex exposure 2. Cloud basics (Azure / AWS / GCP) 3. Basic understanding of CI/CD or MLOps concepts 4. Internship/project experience in GenAI or ML use cases Experience & Qualification 1. Bachelor's in Computer Science / Data Science or related field. 2. 12 years of experience in AI/ML (including internships and project work). 3. Exposure to at least one end-to-end ML/GenAI project (academic and professional).