ML Engineer / Data ScientistMacpower Digital Assets Edge Private Limited • United States
ML Engineer / Data Scientist
Macpower Digital Assets Edge Private Limited
- United States
- United States
À propos
Proven hands-on experience in
Python programming , with expertise in popular AI/ML frameworks such as
TensorFlow, PyTorch, scikit-learn, LangChain , and
LlamaIndex .
Strong background in
building and implementing machine learning models .
Hands-on experience in developing
AI/ML/GenAI solutions
using
AWS services
such as
SageMaker .
Experience with
search algorithms, indexing techniques, summarization , and
retrieval models
for effective information retrieval tasks.
Practical experience with
RAG (Retrieval-Augmented Generation) architecture
and its applications in
Natural Language Processing (NLP) .
Good exposure to
Agentic / Multi-agent frameworks .
End-to-end experience in developing
machine learning and deep learning solutions , including
predictive modeling, applied machine learning , and
natural language processing .
Expertise in
data engineering , including preprocessing and cleaning large datasets using
Python, PySpark , and tools like
Pandas
and
NumPy . Proficient in techniques such as
data normalization, feature engineering , and
synthetic data generation .
Solid understanding of
cloud computing principles
and experience in
deploying, scaling , and
monitoring AI/ML/GenAI solutions
on platforms like
AWS .
Proficient in deploying and monitoring ML solutions using
AWS Lambda, API Gateway , and
ECS , and tracking performance using
CloudWatch .
Experience with
Docker
and containerization technologies.
Strong communication skills, with the ability to explain complex technical concepts to both
technical and non-technical stakeholders , and to collaborate effectively with
cross-functional teams .
Must-Have Qualifications:
A
Master's degree
in
Computer Science or Engineering .
Minimum of
14 years of IT experience .
At least
7 years of experience
as a
Machine Learning Engineer
or
Data Scientist .
Hands-on experience using
Python
and APIs such as
Flask, Django , or
FastAPI .
Practical experience with tools such as
LangChain, LlamaIndex , and
Streamlit .
Experience working with
semi-structured and unstructured data .
Must have implemented at least one use case using
Large Language Models (LLMs) .
Must have experience in
prompt engineering
and
fine-tuning LLMs
using techniques like
LoRA
or
PEFT .
Must have implemented a use case using
RAG architecture .
Experience with a
Multi-agent framework
is a strong plus.
Compétences linguistiques
- English
Avis aux utilisateurs
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