Responsibilities
- Design, build, and maintain robust data pipelines and ML systems for production environments.
- Develop and deploy ML and LLM-based solutions addressing real client business challenges.
- Build and maintain ETL/ELT workflows using modern orchestration and distributed computing tools.
- Implement MLOps practices: CI/CD, automated testing, model monitoring, and experiment tracking.
- Architect and implement cloud-native data and AI/ML solutions, primarily on AWS.
- Collaborate closely with Data Scientists, AI/ML Engineers, Backend Engineers, and client stakeholders.
- Participate in code reviews, contribute to technical documentation, and share knowledge within the team.
- Engage in client-facing discussions to understand requirements and propose technical solutions.
Requirements
- 6+ years of hands-on engineering experience with production systems, not just building POCs.
- Full-stack mindset, comfortable across AI, Backend development, Data, and cloud infrastructure.
- Autonomous working style — you drive work forward without needing heavy process overhead.
- Experience adopting AI tools in day-to-day workflows (e.g. Claude Code, GitHub Copilot, or similar).
- Strong sense of ownership and proactivity; you spot problems before they're handed to you.
- Openness to broadening skills into adjacent areas.
- B2+ English, comfortable collaborating across distributed, multicultural teams.
- Strong Python and SQL skills and solid software engineering fundamentals.
- Hands-on experience with Apache Spark for large-scale data processing.
- Proficiency with cloud data warehouse technologies: Snowflake, Redshift, or ClickHouse.
- Proven experience building batch data workflows with Apache Airflow or similar orchestration tools.
- Experience with real-time data processing using Kafka and streaming frameworks.
- Experience with LLM-based application patterns, including RAG architectures, prompt design, and agentic workflows.
- Basic understanding of embedding models, vector databases, and semantic search.
- Awareness of LLM evaluation techniques and quality assurance approaches.
- Experience deploying and maintaining ML models in production environments.
- Understanding of CI/CD practices applied to ML pipelines.
- Hands-on experience with AWS (SageMaker, Bedrock, Lambda, Glue, S3, ECR, or similar); GCP considered
Nice to Have
- Relevant cloud certifications are a plus.
Additional Information
- B2+ English, comfortable collaborating across distributed, multicultural teams.
