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AI/ML Engineer
2 weeks ago
- Familiarity with MCP (Model Context Protocol), tool calling, and preferably agent-based frameworks (Agent Boot or similar) for building autonomous AI workflows.
Job Summary:
We are seeking an experienced
AI/ML Engineer
with a minimum of 2
years of hands-on experience
in
Natural Language Processing (NLP)
and
LLM (Large Language Model)
-based solutions. The ideal candidate should have a strong background in
Retrieval-Augmented Generation (RAG)
,
vector database design
,
chunking strategies
, and
chatbot development
, along with proven expertise in
fine-tuning and integrating LLMs
for real-world applications.
Key Responsibilities:
· Design and develop
NLP pipelines
to process and extract insights from unstructured data.
· Build and optimize
RAG (Retrieval-Augmented Generation)
workflows for LLM-based systems.
· Implement
chunking strategies
to improve information retrieval and response accuracy.
· Work with
vector databases
(e.g., Pinecone, Weaviate, FAISS) to store and retrieve embeddings efficiently.
· Develop
conversational AI/chatbot
solutions using modern frameworks and APIs.
· Fine-tune, integrate, and deploy
LLMs (Open-source or proprietary)
based on application needs.
· Collaborate with product and engineering teams to translate business requirements into intelligent AI-driven features.
· Conduct experiments, evaluate model performance, and optimize pipelines for scalability and accuracy.
Required Skills & Qualifications:
· years of experience in
AI/ML Engineering
, with a strong focus on
NLP
.
· Proven experience with
RAG frameworks
,
LLM fine-tuning
, and
prompt engineering
.
· Hands-on experience with
vector databases
such as
FAISS, Pinecone, Chroma, Weaviate
, etc.
· Strong understanding of
embedding models
,
tokenization
, and
text chunking techniques
.
· Experience in building
chatbots
and integrating with LLMs (OpenAI, Cohere, HuggingFace, etc.).
· Proficiency in
Python
and libraries like
LangChain, Transformers, PyTorch, or TensorFlow
.
· Experience with deploying models using
APIs
,
microservices
, or containerized solutions.
Nice to Have:
· Experience with
multi-modal models
(e.g., text + images).
· Knowledge of
data privacy
and
security best practices
in AI systems.
· Exposure to
cloud services
specially Azure for AI/ML workloads.