Macpower Digital Assets Edge
ML Engineer / Data Scientist
Macpower Digital Assets Edge, Cupertino, California, United States, 95014
Job Overview:
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
I/ML/GenAI solutions
using
WS 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
gentic / 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
WS . Proficient in deploying and monitoring ML solutions using
WS 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:
Master's degree
in
Computer Science or Engineering . Minimum of
14 years of IT experience . t 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.
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
I/ML/GenAI solutions
using
WS 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
gentic / 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
WS . Proficient in deploying and monitoring ML solutions using
WS 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:
Master's degree
in
Computer Science or Engineering . Minimum of
14 years of IT experience . t 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.