Senior AI/ML Engineer

We are seeking a highly skilled and motivated Senior AI/ML Engineer to join our dynamic team and take part in creating a new product. 

The team is working on an AI Agent to develop and  support business processes for different business areas.

About the project

This project is building an AI platform that helps companies develop complex software faster and with fewer errors.
Instead of writing requirements, documentation, and validation rules manually, the platform does it using AI.
It turns a product idea into detailed, ready-to-implement specifications — and keeps them in sync with tests and validation.
It’s especially valuable for large organizations that work under strict regulations (like finance or healthcare), where precise documentation is critical.

Requirements:

• Experience: Demonstrated experience of at least 5 years in developing AI/ML solutions with a focus on LLM and LLM frameworks (e.g. LangChain). Strong knowledge and practical experience in Generative AI, Interactive Chatbots, Graph databases. Experienced in Python, async input/output, multiprocessing, parallel processing.
• Data Processing: Experience in preprocessing and handling large-scale, unstructured text data, and the ability to perform data analysis and validation.
• Problem-solving and Innovation: Proven ability to tackle complex technical challenges, think innovatively, and propose creative solutions to real-world problems.
• Communication: Excellent verbal and written communication skills to effectively convey complex technical concepts to both technical and non-technical stakeholders.
• English level: Upper-Intermediate

Responsibilities:

• Interact with modern LLMs and adopt best practices for their application in enterprise-grade solutions.
• Conduct R&D activities involving LLMs: generate code based on input descriptions, build test cases from user scenarios, and work with APIs (e.g., OpenAI).
• LLM-Based Systems: Design and optimize LLM pipelines using frameworks like LangChain to support use cases such as requirement extraction, document validation, and test case generation.
• AI-Driven Product Modeling: Build logic for transforming raw product inputs (e.g., documents, interviews, chats) into structured outputs such as specifications, BRDs, test suites, and Jira stories.
• Interactive Intelligence: Develop intelligent and adaptive chat experiences for requirements elicitation and knowledge retrieval, tailored to industries like finance, healthcare, automotive, and telecom.
• Performance Monitoring: Track model performance and iterate to improve accuracy, efficiency, and scalability.
• Graph & Contextual Reasoning: Apply graph databases and context-aware AI models to ensure consistency and traceability across evolving specifications and requirements.
• Scalable Data Processing: Build and maintain data pipelines capable of handling and validating large volumes of unstructured enterprise data.
• Data Preprocessing and Annotation: Collaborate with data engineers and domain experts to prepare and annotate datasets for training AI/ML models.
• Prototyping and Evaluation: Rapidly prototype and evaluate AI/ML solutions, selecting the most effective approaches based on performance metrics.
• Documentation: Maintain clear and thorough documentation of development processes, design choices, and evaluation results.
• Collaboration: Work closely with cross-functional teams including data scientists, software engineers, and domain experts.
• Stay Abreast of Industry Trends: Continuously explore the latest advancements in AI/ML, NLP, LLMs, and related technologies, integrating relevant findings into ongoing work.