Production ML/AI Engineer (AWS, MLOps)

Your ITeams Sp. z o.o.; Polska; Hybrid; PLN 120,000 - 210,000; Full time

Benefits offered by this job

Luxmed Gold Extended medical care

Multisport Plus

Job summary

Your ITeams Sp. z o.o. is seeking an experienced ML/AI Engineer to join a team delivering production-grade AI solutions for a major insurance client.

The role blends ML development, systems design, and integration work across cloud and on-prem environments. You will design, train and deploy models using PyTorch/TensorFlow, AWS SageMaker and Bedrock, implement MLOps practices, and collaborate with backend and business stakeholders to bind technical and business requirements.

Qualifications

• 4+ years of hands-on ML/AI in production environments
• Advanced proficiency in Python
• Experience with PyTorch and TensorFlow for building, training, and fine-tuning models
• Knowledge of MLOps principles and model monitoring/versioning/CI/CD
• Experience with AWS ML services: SageMaker, Bedrock, ECS/EKS
• API integration via REST/Web Services; Swagger UI familiarity; FastAPI knowledge

Responsibilities

• Design, build and maintain ML/AI models in production
• Train models from scratch and fine-tune pre-trained models
• Deploy and scale AI solutions using AWS SageMaker, Bedrock, ECS/EKS
• Design IT solution architectures following best practices
• Integrate models with existing ecosystem via APIs and microservices
• Collaborate with backend and business stakeholders to define requirements
• Ensure code quality, documentation, and ML CI/CD processes

Skills

Python

ML/AI

PyTorch

TensorFlow

MLOps

Tools

SageMaker

Bedrock

ECS/EKS

FastAPI

Job description

Your ITeams Sp. z o.o. is seeking an experienced ML/AI Engineer to join a team delivering production-grade AI solutions for a major insurance client.

The role blends ML development, systems design, and integration work across cloud and on-prem environments. You will design, train and deploy models using PyTorch/TensorFlow, AWS SageMaker and Bedrock, implement MLOps practices, and collaborate with backend and business stakeholders to bind technical and business requirements.