Generative AI Engineer

Job Description Roles & Responsibilities 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: Strong foundation in Python (data handling, APIs, scripts) Understanding of APIs, Docker, or deployment workflows Exposure to deploying ML/AI models (even in projects/internships) Understanding of embeddings, retrieval flow Hands-on exposure through projects Experience working with GPT/Llama APIs Ability to structure prompts and evaluate outputs Vector Databases (Exposure Level) - Familiarity with FAISS / Chroma / Pinecone and basic usage in projects. ML Fundamentals: Core concepts - overfitting, evaluation metrics, basic algorithms. Ability to reason about model behavior. Core Responsibilities (Execution Under Guidance) Assist in building RAG-based GenAI solutions for enterprise use cases. Develop Python-based services/APIs integrating LLMs. Support data preprocessing, embeddings, and retrieval pipelines. Contribute to model deployment and integration tasks. Debug and improve existing pipelines under supervision. Work closely with senior engineers to understand production constraints (latency, cost, accuracy) Good to Have LangChain / LlamaIndex exposure Cloud basics (Azure / AWS / GCP) Basic understanding of CI/CD or MLOps concepts Internship/project experience in GenAI or ML use cases Desired Candidate Profile Must Have: Strong foundation in Python (data handling, APIs, scripts) Understanding of APIs, Docker, or deployment workflows Exposure to deploying ML/AI models (even in projects/internships) Understanding of embeddings, retrieval flow Hands-on exposure through projects Experience working with GPT/Llama APIs Ability to structure prompts and evaluate outputs Vector Databases (Exposure Level) - Familiarity with FAISS / Chroma / Pinecone and basic usage in projects. ML Fundamentals: Core concepts - overfitting, evaluation metrics, basic algorithms. Ability to reason about model behavior. Core Responsibilities (Execution Under Guidance) Assist in building RAG-based GenAI solutions for enterprise use cases. Develop Python-based services/APIs integrating LLMs. Support data preprocessing, embeddings, and retrieval pipelines. Contribute to model deployment and integration tasks. Debug and improve existing pipelines under supervision. Work closely with senior engineers to understand production constraints (latency, cost, accuracy) Good to Have LangChain / LlamaIndex exposure Cloud basics (Azure / AWS / GCP) Basic understanding of CI/CD or MLOps concepts Internship/project experience in GenAI or ML use cases Experience & Qualification Bachelor's in Computer Science / Data Science or related field. 12 years of experience in AI/ML (including internships and project work). Exposure to at least one end-to-end ML/GenAI project (academic and professional). Company Industry IT - Software Services Department / Functional Area IT Software Keywords Generative AI Engineer Get real-time job updates only on our App