GenAI Developer

We are looking for a skilled Generative AI Engineer with 3–5 years of overall experience and hands-on expertise in building AI/ML solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agent frameworks. The candidate will be responsible for designing, developing, and deploying scalable GenAI applications for enterprise use cases.

Experience: 3–5 Years

Location: Bangalore

Responsibilities

We are looking for a skilled Generative AI Engineer with 3–5 years of overall experience and hands-on expertise in building AI/ML solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agent frameworks. The candidate will be responsible for designing, developing, and deploying scalable GenAI applications for enterprise use cases.

Design and develop Generative AI solutions using LLMs such as GPT, Llama, Claude, or similar models.

Build and optimize RAG-based applications using vector databases.

Develop AI agents and intelligent automation workflows using frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.

Fine-tune, evaluate, and deploy machine learning and Generative AI models.

Create APIs and microservices for AI-driven applications.

Work closely with business and technical stakeholders to understand requirements and deliver AI solutions.

Ensure model performance, scalability, security, and responsible AI practices.

Technical requirements

3–5 years of experience in Software Development, AI/ML, or Data Science.

Strong proficiency in Python.

Hands-on experience with Generative AI, LLMs, Prompt Engineering, and RAG.

Experience with LangChain, LangGraph, LlamaIndex, CrewAI, or similar frameworks.

Knowledge of Vector Databases such as Pinecone, ChromaDB, FAISS, or Weaviate.

Experience with cloud platforms (Azure/OpenAI, AWS, or GCP).

Understanding of REST APIs, microservices, and application deployment.

Familiarity with Machine Learning and NLP concepts.

Additional responsibilities

Experience with fine-tuning LLMs and model evaluation.

Exposure to MLOps tools and CI/CD pipelines.

Knowledge of Docker, Kubernetes, and cloud-native deployments.

Strong problem-solving and communication skills.

Education

Bachelor of Engineering