Generative AI Engineer

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 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).