Job description
Job Description:
Design, develop, and maintain applications in
Python .
Implement
RAG pipelines
by integrating
LLMs (OpenAI, Azure OpenAI, Hugging Face, LangChain, LlamaIndex, etc.)
with enterprise and external data sources.
Develop
MCP-based integrations
to connect tools, APIs, and enterprise data systems with LLMs.
Build APIs and microservices for
AI-powered search, summarization, and conversational AI .
Create
document ingestion pipelines
(PDFs, databases, SharePoint, etc.) and manage embeddings with
vector databases (Pinecone, Weaviate, FAISS, Qdrant, Azure Cognitive Search, etc.) .
Collaborate with AI engineers, architects, and data teams to ensure scalable deployment of RAG/MCP solutions.
Optimize application
performance, security, and scalability
for production-grade AI systems.
Stay updated with
AI frameworks, MCP standards, and cloud AI services .
Required Skills & Experience:
Minimum of 8 years of IT experience with 1+ years of AI experience
Strong hands-on experience in
Python .
Solid understanding of
OOP, REST APIs, and microservices architecture .
Proven experience with
LLM-based applications
and
RAG (Retrieval-Augmented Generation)
integration.
Knowledge and practical implementation of
Model Context Protocol (MCP)
for AI tool orchestration.
Familiarity with
vector databases
(FAISS, Pinecone, Weaviate, Qdrant, Azure Cognitive Search).
Hands-on experience with
LangChain, LlamaIndex, Hugging Face Transformers , or similar AI libraries.
Strong problem-solving and cross-functional collaboration skills.
Good to Have:
Experience with
containerization (Docker, Kubernetes) .
Experience with
cloud AI services (Azure, AWS, GCP)
for deployment and scaling.
Exposure to
SQL/NoSQL databases
for structured and unstructured data.
Prior experience in
chatbot development, enterprise search, or knowledge management systems .
Understanding of
MLOps practices
for AI model deployment and monitoring.
Required Skill Profession
Computer Occupations