GenAI Engineer
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About this role
Role Overview
A Generative AI Engineer designs, builds, and deploys AI systems that can generate content such as text, images, code, audio, or video using advanced models like large language models (LLMs) and diffusion models.
🔧 Key Responsibilities
Develop and deploy applications using LLMs (e.g., GPT-based systems)
Fine-tune and optimize foundation models for specific use cases
Build AI-powered features like chatbots, copilots, and automation tools
Design prompt engineering strategies and evaluation pipelines
Integrate AI models with APIs, databases, and production systems
Work on Retrieval-Augmented Generation (RAG) pipelines
Ensure model performance, scalability, and reliability
Monitor outputs for quality, bias, and safety
Collaborate with data scientists, backend engineers, and product teams
🛠️ Required Skills
Core Technical Skills
Strong programming in Python
Experience with ML frameworks like:
TensorFlow / PyTorch
Familiarity with:
LLMs (GPT, LLaMA, Claude, etc.)
Prompt engineering
Embeddings & vector databases (FAISS, Pinecone)
Knowledge of NLP concepts
API development (FastAPI, Flask)
GenAI-Specific Skills
RAG (Retrieval-Augmented Generation)
Fine-tuning & model evaluation
LangChain / LlamaIndex (or similar frameworks)
Working with OpenAI, Anthropic, or open-source models
Cloud & Tools
AWS / GCP / Azure
Docker & CI/CD pipelines
Git version control
📊 Preferred Qualifications
Bachelor’s/Master’s in Computer Science, AI, or related field
Experience building AI products in production
Understanding of AI ethics and responsible AI practices
💼 Nice-to-Have
Experience with multimodal AI (text + image/audio)
Knowledge of vector search systems
Exposure to MLOps practices
🎯 Example Use Cases You Might Work On
AI chatbots or virtual assistants
Code generation tools
Document summarization systems
AI content generators (text/image/video)
Enterprise knowledge assistants
Why we're hiring
🧠 Generative AI Engineer – Job Description Role Overview A Generative AI Engineer designs, builds, and deploys AI systems that can generate content such as text, images, code, audio, or video using advanced models like large language models (LLMs) and diffusion models. 🔧 Key Responsibilities Develop and deploy applications using LLMs (e.g., GPT-based systems) Fine-tune and optimize foundation models for specific use cases Build AI-powered features like chatbots, copilots, and automation tools Design prompt engineering strategies and evaluation pipelines Integrate AI models with APIs, databases, and production systems Work on Retrieval-Augmented Generation (RAG) pipelines Ensure model performance, scalability, and reliability Monitor outputs for quality, bias, and safety Collaborate with data scientists, backend engineers, and product teams 🛠️ Required Skills Core Technical Skills Strong programming in Python Experience with ML frameworks like: TensorFlow / PyTorch Familiarity with: LLMs (GPT, LLaMA, Claude, etc.) Prompt engineering Embeddings & vector databases (FAISS, Pinecone) Knowledge of NLP concepts API development (FastAPI, Flask) GenAI-Specific Skills RAG (Retrieval-Augmented Generation) Fine-tuning & model evaluation LangChain / LlamaIndex (or similar frameworks) Working with OpenAI, Anthropic, or open-source models Cloud & Tools AWS / GCP / Azure Docker & CI/CD pipelines Git version control 📊 Preferred Qualifications Bachelor’s/Master’s in Computer Science, AI, or related field Experience building AI products in production Understanding of AI ethics and responsible AI practices 💼 Nice-to-Have Experience with multimodal AI (text + image/audio) Knowledge of vector search systems Exposure to MLOps practices 🎯 Example Use Cases You Might Work On AI chatbots or virtual assistants Code generation tools Document summarization systems AI content generators (text/image/video) Enterprise knowledge assistants