Вакансия ML/LLM Engineer (with software engineering experience)

Skills

• Python
• Hugging Face Transformers
• LangChain
• PyTorch
• TensorFlow
• XGBoost
• Random Forest
• Logistic Regression
• AWS
• AWS Lambda
• Amazon S3
• AWS SageMaker
• RAG
• LLM
• CI/CD
• NLP

Описание вакансии

Intelliarts is seeking a Machine Learning Engineer to join our growing ML Team.
We are a cross-functional team focused on advancing Intelliarts’ capabilities in the Machine Learning domain. In this role, you’ll collaborate closely with current and prospective clients to explore how ML can address their business challenges – through research, prototyping, and deploying innovative solutions.
You’ll join a team that is currently particularly focused on Large Language Models (LLMs), helping organizations leverage AI to enhance efficiency and automation. Our track record includes building impactful ML-driven applications that have contributed to over $1 billion in client revenue.
This position is ideal for someone with a strong foundation in general machine learning and LLM technologies, excellent problem-solving skills, and the ability to clearly communicate complex technical ideas – particularly to clients based in the USA.

Responsibilities

• Leverage transformer-based models (e.g., GPT, BERT, LLaMA) for domain-specific tasks such as content generation, summarization, and semantic search.
• Build scalable, production-grade RAG pipelines using tools like LangChain, LlamaIndex, or custom frameworks.
• Design and deploy vector search solutions using tools such as FAISS, Qdrant, or Pinecone; optimize dense retrieval pipelines for recall, relevance, and latency.
• Collaborate with DevOps to productionize ML models, ensuring reliability, monitoring, and scalability using cloud infrastructure (AWS preferred).
• Build robust data pipelines to support ML use cases – including indexing, tokenization, and dataset preparation.
• Develop and optimize traditional machine learning models (e.g., tree-based, linear models) for use cases such as classification, regression, ranking, or forecasting.
• Apply best practices in ML model validation, including data splitting strategies (e.g., time-based, stratified), cross-validation, performance tracking, and monitoring in production.
• Work closely with data scientists, software engineers, and product stakeholders to deliver and iterate on ML-based products.

Personal Profile Overview

• Degree in Data Science, Computer Science, Software Engineering or related field
• Stability in previous employment history with a tendency to remain with employers for extended periods
• Experience in managing diverse project activities (not just coding, but also requirements analysis, preparing estimations)
• Clear and effective communication skills, both verbal and written, and ability to convey ideas, information, and messages accurately and efficiently
• Proficiency in fostering effective collaboration and teamwork activities
• Ability to analyze information, assess situations, and make decisions based on sound reasoning and logical evaluation
• Focus on delivering exceptional customer experiences and prioritizing customer satisfaction
• Analytical thinking, problem-solving abilities, and strategic approach to technical challenges
• Transparency in sharing information within a team and company
• Willingness to acquire new knowledge and insights to enhance professional growth and performance

Required skills

• 4+ years of experience in ML or Data Science roles, with at least 1 year focused on LLM or NLP-based systems.
• Hands-on experience with Python and libraries like HuggingFace Transformers, LangChain, PyTorch, TensorFlow, or similar.
• Experience developing and validating classic ML models (e.g., XGBoost, Random Forest, Logistic Regression) for structured or tabular data.
• Strong grasp of model evaluation techniques: precision, recall, ROC-AUC, calibration, error analysis, etc.
• Proven track record of building and deploying ML models into production environments.
• Deep understanding of machine learning algorithms, embeddings, and transformer architectures.
• Solid knowledge of retrieval methods, vector search engines, and hybrid search strategies.
• Familiarity with cloud platforms (AWS strongly preferred), including model hosting, Lambda, S3, or SageMaker.

As a plus

• Experience with fine-tuning large models on custom datasets.
• Knowledge of RAG architectures, search augmentation techniques, and content attribution workflows.
• Experience with prompt engineering, few-shot learning, or alignment techniques.
• Experience designing robust ML pipelines, including model drift detection, retraining workflows, and monitoring systems.
• Familiarity with LLM evaluation methods, hallucination detection, or explainability.
• Understanding of software engineering practices (version control, CI/CD, testing).
• Contributions to open-source AI projects or published work in NLP/LLM areas.