Job Description
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Cyma Systems Inc, is seeking a talented Generative AI & Data Science Engineer. Apply via Dice today!
Title
Generative AI & Data Science Engineer
Location
Basking Ridge, NJ (Hybrid: 3 days in the office a week)
About the Role
We are looking for a versatile and driven Generative AI & Data Science Engineer to join our growing AI/ML team. This hybrid role involves working at the forefront of LLM-based multi-agent systems and data-driven modeling, combining technologies like LangGraph, RAG, VectorDBs, and cloud ML platforms (Google Cloud Platform, AWS) with classical data science practices including predictive modeling, experimentation, and statistical analysis.
Key Responsibilities
Generative AI / Multi-Agent Systems
- Build and manage multi-agent GenAI applications using LangGraph, including agent orchestration, memory handling, and dynamic routing.
- Design and implement RAG pipelines for enterprise search, intelligent assistants, and task automation, leveraging best practices in chunking and embedding generation.
- Integrate with Vector Databases (e.g., Pinecone, Weaviate, FAISS) to enable scalable semantic search and retrieval.
- Fine-tune LLMs using techniques like LoRA, PEFT, and parameter-efficient tuning for domain-specific use cases.
- Deploy GenAI systems using Vertex AI (Google Cloud) and AWS ML services (SageMaker, Bedrock, Lambda).
Data Science & Analytics
- Conduct exploratory data analysis, build predictive and statistical models (e.g., regression, classification, clustering), and generate actionable insights.
- Design and execute experiments (A/B testing) to validate feature changes, GenAI-driven enhancements, and ML model outputs.
- Collaborate with stakeholders to translate business problems into data and ML-driven solutions.
- Develop and automate dashboards, reporting pipelines, and model monitoring tools for continuous feedback loops.
- Support feature engineering, data wrangling, and data quality analysis using SQL, Pandas, and other data science libraries.
Required Skills and Qualifications
- Bachelor's or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
- 3-6 years of experience in Data Science and AI/ML Engineering roles.
- Hands-on experience with LangGraph, LangChain, and/or similar LLM orchestration frameworks.
- Solid foundation in machine learning, statistical modeling, and data analytics.
- Proficiency with Python, including libraries like Scikit-learn, Pandas, NumPy, PyTorch, or TensorFlow.
- Strong SQL skills and experience working with structured and unstructured data.
- Experience deploying ML/GenAI models on cloud platforms (Vertex AI, Google Cloud Platform, AWS).
- Working knowledge of RAG, fine-tuning, and vector embeddings.
Preferred Qualifications
- Familiarity with agent frameworks like CrewAI, AutoGen, or Langgraph.
- Experience integrating LLMs into data pipelines, BI tools, or decision support systems.
- Contributions to open-source GenAI, data science projects, or publications.
- Understanding of model governance, responsible AI, and cloud cost optimization.
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Job Highlights
Qualifications
- Bachelor’s or Master’s degree in related fields
- 3-6 years of experience in Data Science and AI/ML roles
- Hands-on experience with LangGraph, LangChain, or similar frameworks
- Solid foundation in machine learning, statistical modeling, and data analytics
- Proficiency with Python libraries (Scikit-learn, Pandas, NumPy, PyTorch, TensorFlow)
- Strong SQL skills
- Experience with cloud deployment (Vertex AI, Google Cloud, AWS)
- Working knowledge of RAG, fine-tuning, vector embeddings
Responsibilities
- Work on LLM-based multi-agent systems and data-driven modeling integrating technologies like LangGraph, RAG, VectorDBs, and cloud ML platforms.
- Build and manage multi-agent GenAI applications.
- Design RAG pipelines for various enterprise applications.
- Integrate with Vector Databases for semantic search.
- Fine-tune LLMs for specific domains.
- Deploy GenAI systems on cloud platforms.
- Perform exploratory data analysis, build models, and generate insights.
- Conduct experiments and validate model improvements.
- Collaborate with stakeholders to develop ML solutions.
- Develop dashboards, pipelines, and monitoring tools.
- Engage in feature engineering and data quality improvements.