The Role
Design and maintain data pipelines and ML systems, implement MLOps practices, and collaborate with teams to address client challenges in AI and data projects.
Summary Generated by Built In
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.
Experience
Data Engineering
LLMs & Generative AI / MLOps
Cloud & Infrastructure
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.
Skills Required
- 6+ years of hands-on engineering experience with production systems
- Strong Python and SQL skills
- Hands-on experience with Apache Spark
- Proficiency with cloud data warehouse technologies
- Experience building batch data workflows with Apache Airflow
- Experience with real-time data processing using Kafka
- Experience deploying and maintaining ML models in production
- Hands-on experience with AWS (SageMaker, Bedrock, etc.)
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The Company
What We Do
Provectus is an Artificial Intelligence consultancy and solutions provider, helping businesses achieve their objectives through AI. We are recognized by industry think tanks as a leading provider of AI solutions in specific business domains, driven by sophisticated IT service management and tech innovation. Provectus is a value driver and a trusted partner for our clients and employees. Provectus is an AWS Premier Consulting Partner with competencies in Data & Analytics, DevOps, and Machine Learning. We design and build AI solutions for industry-specific use cases, Data and Machine Learning foundation, Cloud transformation, and DevOps adoption.
