Senior Data Engineer (with AI)

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
  Relevant cloud certifications are a plus.

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.

Why Provectus?

• Impactful work: projects span GenAI, MLOps, and NextGen data platforms for global enterprises across multiple industries.
• Senior-calibre peers: collaborate with top ML and Data professionals across North America, LATAM, and EMEA.
• Career growth: a clear path toward Tech Lead if you have the ambition — we actively develop our engineers.
• Recognised expertise: AWS Premier Consulting Partner featured in Forrester’s AI Technical Services Landscape.