We are building a generational application for support, sales and data analysis functionality. It uses chat, agents and functional calls to external systems.

-       2+ years of professional experience with Python;

-       1+ years AI agentic systems experience

-       Experience using Generative AI, LLMs, RAG and vector databases in production;

-       Ability to write production level code in Python;

-       Experience in LLMs agents development for specific multiple tasks; 

-       Experience of functional calling to connection of external tools and systems; 

-       Experience with LLM frameworks (i.e. LangChain, LlamaIndex) and prompt engineering techniques;

-       Experience with the best practices in software development, including version control, testing, and continuous integration;

-       Fundamental knowledge of Data Science;

-       Familiarity with the open-source ecosystem, frameworks, and libraries.

- Experience with Machine Learning Operations

- End-to-end experience in collection data, fine tuned, training, evaluating, testing, and deploying Generative app solutions in production;

- Background in building back-end microservices and data platforms using Python.

• Write production code that meets high-quality and maintainability standards;

• Create and developing AI Agents system;

• Selecting the most appropriate Generative AI, Natural Language Processing (NLP), and Machine Learning(ML) model depending on the use case;

• Use your prompt engineering and prompt chaining skills to create new prompts and keep improving on the existing ones;

• Deploy services in production environments;

• Develop and implement strategies for prompt engineering, model refinement, and training pipelines to enhance model performance;

• Manage the integration of сompany knowledge into NLP models to improve contextual understanding and output relevance;

• Evaluate and utilize state-of-the-art embedding vectors and encoding methods to ensure optimal model performance;

• Guide the team in the expansion and refinement of taxonomies using large language models, followed by human review for tagging accuracy;

• Drive the adoption of best practices in NLP model development, deployment, and maintenance, staying abreast of the latest industry trends and research;

• Improve our LLMOps infrastructure to have a solid feedback loop with the most appropriate metrics to keep optimizing each use case;

Computer Science, Engineering, Data science, Computational linguistics

 or similar

Python, SQL, Fastapi, Celery, W&B, Docker, AWS: SQS, S3; APP runner, ECR; CircleCI, Docker Hub, Bitbucket, Localstack, LangGraph, LangChain, LlamaIndex, OpenAI, Mistral, llamas models;