Senior ML Engineer: RAG/LLM Systems & Rapid Prototyping

Grid Dynamics; United States; On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flexible schedule

Medical insurance

Vision and dental

Professional development

Well-equipped office

Job summary

Grid Dynamics is seeking a Machine Learning Engineer who can own projects from problem definition through production, design evaluation pipelines, and advance LLM-based systems including RAG and agents.

You will collaborate with product, engineering, and research teams to translate business goals into measurable ML objectives, iterate rapidly in ambiguous environments, and clearly communicate findings and trade-offs to both technical and non-technical stakeholders.

Qualifications

• 5+ years of experience in machine learning engineering or a related field.
• Strong Python and ML framework experience; knowledge of model evaluation.
• Experience with LLMs, RAG architectures, and AI safety concepts.
• Ability to translate business goals into measurable ML objectives.
• Experience building ML pipelines and evaluation infrastructure.

Responsibilities

• Own machine learning projects from problem definition through implementation.
• Design and implement evaluation methodologies for AI/ML systems.
• Create datasets, benchmarks, and metrics to measure model and product performance.
• Evaluate and improve LLM-based systems, including RAG applications and safety systems.
• Collaborate with product, engineering, and research teams to translate goals into ML objectives.
• Prototype and iterate rapidly to solve business challenges.
• Communicate findings and trade-offs to technical and non-technical stakeholders.

Skills

Python

ML fundamentals

Model evaluation

LLMs/RAG

ML pipelines

Communication

Education

Bachelor's degree in Computer Science

Tools

PyTorch

TensorFlow

JAX

Job description

Grid Dynamics is seeking a Machine Learning Engineer who can own projects from problem definition through production, design evaluation pipelines, and advance LLM-based systems including RAG and agents.

You will collaborate with product, engineering, and research teams to translate business goals into measurable ML objectives, iterate rapidly in ambiguous environments, and clearly communicate findings and trade-offs to both technical and non-technical stakeholders.