AI Engineer

Applicants must be able to work directly for Artech on W2. Day-to-Day Responsibilities

• Lead the design, development, and implementation of complex data management and storage applications, often using new technologies.
• Exercise judgment within broadly defined practices and policies to select methods, techniques, and evaluation criteria for obtaining results.
• Provide work leadership by assigning work and resolving problems.
• Communicate effectively with multiple business stakeholders, IT, and external partners.
• Execute high-value, enterprise projects with clear articulation of benefits, costs, and risks.

Company Benefits & Culture

• Opportunities to work with modern technologies as a disruptive influence within the finance domain.
• Superb communication, coaching, and management skills development.
• Collaborative and innovative work environment.

We are seeking a hands-on AI Engineer to work on a high visibility Generative AI project. This project involves summarization and chatbot development to support a large volume of documents from various lines of business and of various types. The ideal candidate is a self-starter with strong technical skills, leadership qualities, and the ability to effectively communicate complex technical concepts., * 2 years of professional experience as a data scientist executing end-to-end Generative AI projects using technologies such as LLM Models (Llama, Mixtral, Mistral), various inferencing mechanisms (transformer/pipeline, vllm), and agentic framework (langgraph).

• Experience with evaluation frameworks: RAGAS, langsmith, phoenix-arize and strong programming experience with Python.
• Proficiency in embedding technologies (all-MiniLM-L6-v2, all-mpnet-base-v2) and LLM development frameworks (Langchain, LlamaIndex).
• Experience with REST API, FastAPI, and Websockets and with Enterprise Redis Database, Qdrant, and Cockroach.

Preferred Skills & Qualifications

• Angular experience is a huge plus.
• Experience with Slurm, Hadoop/Hive, Neo4J, Apache Spark, Kafka, and MongoDB is a plus.
• Knowledge of machine-learning techniques, classifiers, and statistical methods.
• Advanced degree, or equivalent experience, in mathematics, data science, or other quantitative disciplines.
• Prior work experience at the client or in the client’s industry.