SynapOne
Overview
We are seeking
hands-on Data Scientists
to support next-generation AI initiatives. Two positions focus on
Generative AI/NLP , and one focuses on
Fraud Detection & Time Series Analysis . We are looking for
PhD-level (preferred)
candidates who are passionate about applied AI, NLP, and advanced modeling — individuals who are hands-on builders, not leadership roles. Key Responsibilities
Design and implement
machine learning ,
deep learning , and
AI models
for real-world problems. Develop and fine-tune
Generative AI
and
NLP
applications using
LLMs
(GPT-4, Claude, Llama, Mistral, etc.). Apply
RAG ,
LoRA ,
PEFT , and
LangChain
for retrieval augmentation and fine-tuning. Work with
Vector Databases ,
Knowledge Graphs , and
Graph-based AI architectures . Handle
structured and unstructured data
using
PySpark ,
AWS SageMaker , and related tools. Build and maintain
CI/CD pipelines
(Git, Jenkins, GitLab). Collaborate with cross-functional teams to translate ideas into scalable production AI systems. Minimum Qualifications
Education:
MS in Computer Science, Statistics, Mathematics, or related field ( PhD highly preferred ). 3+ years
of experience building and deploying ML/DL models. Proficiency with: Python (NumPy, SciPy, PySpark, Scikit-learn) AWS SageMaker, Jupyter Notebooks NLP tools:
SpaCy, NLTK, BERT, RoBERTa, OpenAI APIs Deep Learning frameworks:
TensorFlow, PyTorch, Keras Experience in
Generative AI, NLP/NLG , and
LLM Fine-Tuning . Strong
SQL
and
data pipeline
development skills. Familiar with
data visualization tools
like Tableau, Kibana, or QuickSight. Preferred Qualifications
Experience with
LLM Agents ,
Agentic Programming , and
Human-in-the-Loop (HITL)
systems. Background in
fraud detection ,
anomaly detection , or
time series forecasting . Experience with
Docker ,
Kubernetes ,
ElasticSearch/OpenSearch . Exposure to
GraphRAG ,
Chain-of-Thought (CoT) , and
Knowledge Graphs (OWL, RDF, SPARQL) . Ideal Candidate
PhD or advanced MS with applied or published AI/ML research. Hands-on, self-starter mindset with strong problem-solving and experimentation skills. Passion for innovation in
Generative AI ,
NLP , and
predictive analytics .
#J-18808-Ljbffr
We are seeking
hands-on Data Scientists
to support next-generation AI initiatives. Two positions focus on
Generative AI/NLP , and one focuses on
Fraud Detection & Time Series Analysis . We are looking for
PhD-level (preferred)
candidates who are passionate about applied AI, NLP, and advanced modeling — individuals who are hands-on builders, not leadership roles. Key Responsibilities
Design and implement
machine learning ,
deep learning , and
AI models
for real-world problems. Develop and fine-tune
Generative AI
and
NLP
applications using
LLMs
(GPT-4, Claude, Llama, Mistral, etc.). Apply
RAG ,
LoRA ,
PEFT , and
LangChain
for retrieval augmentation and fine-tuning. Work with
Vector Databases ,
Knowledge Graphs , and
Graph-based AI architectures . Handle
structured and unstructured data
using
PySpark ,
AWS SageMaker , and related tools. Build and maintain
CI/CD pipelines
(Git, Jenkins, GitLab). Collaborate with cross-functional teams to translate ideas into scalable production AI systems. Minimum Qualifications
Education:
MS in Computer Science, Statistics, Mathematics, or related field ( PhD highly preferred ). 3+ years
of experience building and deploying ML/DL models. Proficiency with: Python (NumPy, SciPy, PySpark, Scikit-learn) AWS SageMaker, Jupyter Notebooks NLP tools:
SpaCy, NLTK, BERT, RoBERTa, OpenAI APIs Deep Learning frameworks:
TensorFlow, PyTorch, Keras Experience in
Generative AI, NLP/NLG , and
LLM Fine-Tuning . Strong
SQL
and
data pipeline
development skills. Familiar with
data visualization tools
like Tableau, Kibana, or QuickSight. Preferred Qualifications
Experience with
LLM Agents ,
Agentic Programming , and
Human-in-the-Loop (HITL)
systems. Background in
fraud detection ,
anomaly detection , or
time series forecasting . Experience with
Docker ,
Kubernetes ,
ElasticSearch/OpenSearch . Exposure to
GraphRAG ,
Chain-of-Thought (CoT) , and
Knowledge Graphs (OWL, RDF, SPARQL) . Ideal Candidate
PhD or advanced MS with applied or published AI/ML research. Hands-on, self-starter mindset with strong problem-solving and experimentation skills. Passion for innovation in
Generative AI ,
NLP , and
predictive analytics .
#J-18808-Ljbffr