Full-Stack AI Engineer
3 days ago
We are looking for a highly skilled
Full-Stack AI Engineer
who can design, develop, and deploy advanced AI-driven applications. The ideal candidate will have strong expertise in
RAG (Retrieval-Augmented Generation)
systems,
vector databases
, and
end-to-end ML pipelines
. You will be working on scalable, production-grade AI solutions leveraging state-of-the-art frameworks and cloud-native technologies.
Key Responsibilities
- Design and implement
RAG-based AI applications
with optimized semantic search. - Build and manage
vector database solutions
(Pinecone, Weaviate, Qdrant, FAISS). - Develop and maintain
production-ready ML systems
ensuring performance and scalability. - Apply
prompt engineering techniques
to fine-tune model interactions. - Handle
document chunking, embedding generation, and retrieval optimization
. - Integrate
LangChain/LlamaIndex
frameworks for orchestration. - Implement
caching strategies
(Redis or similar) for efficiency. - Collaborate with cross-functional teams to deliver end-to-end AI solutions.
Core Skills
- Strong experience in
RAG implementation
. - Expertise in
vector databases
(Pinecone, Weaviate, Qdrant, FAISS). - Proficiency in
semantic search optimization
. - Hands-on knowledge of
production ML system deployment
.
Technical Requirements
- Proficient in
LangChain, LlamaIndex
. - Experience with
OpenAI APIs, Hugging Face, Sentence Transformers
. - Strong programming skills in
Python, NumPy
, and ML libraries. - Familiarity with
prompt engineering
best practices. - Knowledge of
document chunking techniques
for retrieval systems. - Experience with
Redis or other caching mechanisms
.
Preferred Qualifications
- 3–6 years of experience in
AI/ML engineering or full-stack development
. - Strong problem-solving and optimization skills.
- Experience working in
fast-paced startup or product environments
. - Knowledge of
cloud deployment (AWS, GCP, or Azure)
is a plus.
Why Join Us?
- Work with cutting-edge
Generative AI and Retrieval systems
. - Opportunity to contribute to
high-impact AI products
in production. - Collaborative, growth-oriented, and innovative work culture.
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