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Generative AI Engineer
2 weeks ago
About the Role
We are looking for a
Generative AI Engineer
with
strong software engineering skills and hands-on experience in developing AI-powered applications
. This role involves
coding AI-driven solutions, developing APIs, integrating models into products, and building scalable AI systems
for production use.
Key Responsibilities:
·
Develop AI-driven applications
by integrating LLMs and generative models into real-world software solutions.
·
Design and build APIs for LLMs and GenAI models
(FastAPI, Flask, Django).
·
Write production-ready code
to integrate AI features into web applications, chatbots, document processing tools, and recommendation systems.
·
Fine-tune and optimize LLMs
(GPT, LLaMA, Gemini, Claude, Mistral) for performance in real-time applications.
·
Develop AI-powered chatbots, document extraction tools, and automation systems
using LangChain, Haystack, or Semantic Kernel.
·
Implement vector search
using FAISS, ChromaDB, or Pinecone for
retrieval-augmented generation (RAG)
applications.
·
Work with databases (PostgreSQL, MongoDB, Redis)
to manage AI-driven data workflows.
·
Build scalable AI microservices and integrate them into enterprise applications
.
·
Collaborate with backend engineers, data engineers, and DevOps teams
to ensure seamless deployment of AI models.
Required Skills & Qualifications:
·
Bachelor's or Master's in Computer Science, Software Engineering, AI, or related field
.
·
6+ years of experience in software development, AI engineering, or full-stack AI application development
.
·
Strong Python coding skills
with experience in frameworks like FastAPI, Flask, Django.
·
Hands-on experience developing APIs for AI models
and integrating them into real-world applications.
·
Experience with LLM APIs
(OpenAI, Gemini, Claude, Mistral, etc.) and fine-tuning custom models.
·
Strong knowledge of vector databases (FAISS, ChromaDB, Pinecone) for AI-powered search
.
·
Experience working with WebSockets, asynchronous programming, and RESTful APIs
.
·
Database experience
with PostgreSQL, MongoDB, or Redis for AI-driven applications.
·
Experience with MLOps pipelines (MLflow, Airflow, Prefect) for model retraining and monitoring
.
Good to have Skills & Qualifications:
·
Familiarity with containerization (Docker, Kubernetes) and cloud-based AI deployments (AWS, GCP, Azure)
.
· Basics of
probabilistic models, deep learning techniques, NLP, and embeddings
.