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Senior AI Engineer
Skills
Continuous Integration And Continuous DeliveryArtificial IntelligenceData ScienceEngineeringGitHTMLMachine Learning
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 4-7 years of experience in AI Engineering Machine Learning Engineering or Data Engineering
- Strong programming skills in Python Java and SQL
- Proven experience designing and building production-grade ML systems and data pipelines
- Experience with Databricks Apache Spark or similar distributed data processing frameworks
- Strong understanding of the machine learning lifecycle including training deployment monitoring and retraining
- Experience with cloud platforms AWS Azure or GCP
- Solid knowledge of data architecture data modeling and data warehousing concepts
- Experience with ML frameworks such as TensorFlow PyTorch or scikit-learn
- Familiarity with MLOps practices and tools such as MLflow Airflow or CI/CD pipelines
- Experience with version control Git and CI/CD development workflows
- Strong problem-solving communication and cross-functional collaboration skills
- Experience with LLMs NLP or generative AI applications
- Experience building end-to-end AI products or data-driven platforms
- Familiarity with real-time or streaming data pipelines
- Experience with cost optimization and performance tuning in Databricks
- Exposure to orchestration tools such as Airflow or Dagster
- Experience mentoring or onboarding junior team members
Nice to have
- Analytical
- Collaborative
- Detail-oriented
- Innovative
- Proactive
Day to day
- Design, build, and deploy scalable AI and machine learning systems in production environments
- Collaborate with Product Managers Data Scientists and Engineers to integrate models into data pipelines and agentic applications
- Develop and optimize reliable high-performance data pipelines while improving platform efficiency scalability and data quality