Senior Software Engineer (Machine Learning)

TL;DR

Senior Machine Learning Engineer (AI/ML): Design scalable data pipelines and infrastructure for enterprise ML systems with an accent on production deployment, MLOps, and reliability. Focus on implementing ML models, optimizing deployments with CI/CD, and evaluating technologies for performance improvements.

Location: Remote 100%

Company

hirify.global (USA) is a dynamic team of software engineering specialists providing high-impact development services to Fortune 500 companies from kernel to cloud.

What you will do

• Design scalable data pipelines and infrastructure for enterprise ML systems
• Implement ML models and systems into production
• Deploy scalable tools and services for machine learning training and inference
• Review, refactor, optimize, containerize, deploy, and monitor ML models
• Implement monitoring and alerting solutions for ML systems reliability
• Collaborate with cross-functional teams to troubleshoot issues and drive innovation

Requirements

• Bachelor's or Master’s degree in Computer Science or related field
• 4+ years experience as Software Engineer, ML Engineer, Data Scientist, AI Engineer, or Data Engineer
• Strong knowledge of Python and machine learning algorithms, data pre-processing, ML frameworks (PyTorch, TensorFlow, Keras)
• Experience with containers, Kubernetes in cloud (AWS, Azure, GCP)
• Familiarity with data workflow orchestration (KubeFlow, Airflow, Argo)
• Understanding of software testing, benchmarking, CI principles, and ability to translate business needs to technical requirements

Nice to have

• Familiarity with agent frameworks (Langchain, Langgraph, LlamaIndex)
• Experience developing RAG systems and Natural Language Processing (NLP)
• Familiarity with monitoring tools (DataDog, Langfuse)
• Any associate cloud certification (AWS preferred)

Culture & Benefits

• Competitive compensation with benefits, paid vacation, and sick leave
• Work with globally diverse team of top engineering talent on toughest challenges
• Ultra-flexible conditions: work from home with equipment allowance, optional office/coworking, no business travel
• Start-up work environment with opportunities for professional growth
• Flexible working hours in remote-first company focused on results