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. Seniority level
Director Employment type
Contract Job function
Engineering, Information Technology, and Science Industries: IT Services and IT Consulting, Technology, Information and Media, and Software Development
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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. Seniority level
Director Employment type
Contract Job function
Engineering, Information Technology, and Science Industries: IT Services and IT Consulting, Technology, Information and Media, and Software Development
#J-18808-Ljbffr