Data Scientist / Gen AI Lead Consultant

Qualifications

• This role is ideal for a mid-level engineer with strong technical depth, a passion for building, and the ability to lead small teams or workstreams in a fast-paced, innovation-driven environment
• At least 7 years of Information Technology experience
• At least 4 years of hands-on GenAI / Agentic AI and data science with machine learning
• Strong proficiency in Python programming
• Experience of deploying the Gen AI applications with one of the Agent Frameworks like Langgraph, Autogen, Crew AI
• Experience in deploying the Gen AI stack/services provided by various platforms such as AWS, GCP, Azure, IBM Watson
• Experience in Generative AI and working with multiple Large Language Models and implementing Advanced RAG based solutions
• Experience in processing/ingesting unstructured data from PDFs, HTML, Image files, audio to text etc
• Experience with data gathering, data quality, system architecture, coding best practices
• Hands-on experience with Vector Databases (such as FAISS, Pinecone, Weaviate, or Azure AI Search)
• Experience with Lean / Agile development methodologies
• Good, applied statistics skills, such as distributions, statistical testing, regression, etc

Responsibilities

• Client seeking a hands-on Gen AI / Agentic AI Lead to drive the development and deployment of next-generation AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks

Full Description

Hi,

Hope you are doing good!

We have a Full-time opportunity for you as Data Scientist / Gen AI Lead Consultant @ Raleigh, NC

Role: Data Scientist / Gen AI Lead Consultant

Locations: Raleigh, NC

Type of Hiring: FTE

Job Description:

Client seeking a hands-on Gen AI / Agentic AI Lead to drive the development and deployment of next-generation AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks.

This role is ideal for a mid-level engineer with strong technical depth, a passion for building, and the ability to lead small teams or workstreams in a fast-paced, innovation-driven environment.

Required Qualifications:

• At least 7 years of Information Technology experience

• At least 4 years of hands-on GenAI / Agentic AI and data science with machine learning

• Strong proficiency in Python programming.

• Experience of deploying the Gen AI applications with one of the Agent Frameworks like Langgraph, Autogen, Crew AI.

• Experience in deploying the Gen AI stack/services provided by various platforms such as AWS, GCP, Azure, IBM Watson

• Experience in Generative AI and working with multiple Large Language Models and implementing Advanced RAG based solutions.

• Experience in processing/ingesting unstructured data from PDFs, HTML, Image files, audio to text etc.

• Experience with data gathering, data quality, system architecture, coding best practices

• Hands-on experience with Vector Databases (such as FAISS, Pinecone, Weaviate, or Azure AI Search).

• Experience with Lean / Agile development methodologies

Preferred Data Scientist Qualifications:

• 4 years of hands-on experience with more than one programming language; Python, R, Scala, Java, SQL

• Hands-on experience with CI/CD pipelines and DevOps tools like Jenkins, GitHub Actions, or Terraform.

• Proficiency in NoSQL and SQL databases (PostgreSQL, MongoDB, CosmosDB, DynamoDB).

• Deep Learning experience with CNNs, RNN, LSTMs and the latest research trends

• Experience in Python AI/ML frameworks such as TensorFlow, PyTorch, or LangChain.

• Strong understanding and experience of LLM fine-tuning, local deployment of open-source models

• Proficiency in building RESTful APIs using FastAPI, Flask, or Django.

• Experience in Model evaluation tools like DeepEval, FMeval, RAGAS , Bedrock model evaluation.

• Experience with perception (e.g. computer vision), time series data (e.g. text analysis)

• Big Data Experience strongly preferred, HDFS, Hive, Spark, Scala

• Data visualization tools such as Tableau, Query languages such as SQL, Hive

Good, applied statistics skills, such as distributions, statistical testing, regression, etc.