Senior Data & AI Tech Lead - Azure & GenAI Expert

Decskill

Lisboa

Hybrid

EUR 60,000 - 90,000

Full time

2 days ago
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Benefits offered by this job

International projects
Growth opportunities
People-first culture
Ownership mindset

Job summary

Decskill seeks a Tech Lead Data & AI Developer to design, build, and optimize data solutions using Python, PySpark, Databricks, and Azure Data Factory. You will craft data pipelines, ML workflows, and GenAI implementations within Azure, ensuring security and governance.

Expect multi-architecture work and an Agile global team collaboration across Lisbon and beyond. You will work on REST APIs, SQL databases like PostgreSQL, and model deployments, while upholding best practices, testing with

Qualifications

  • Bachelor's degree in Software Engineering, Computer Engineering, Computer Science, or a similar field.
  • Hands-on experience with Python, PySpark, and Databricks.
  • Solid experience with Microsoft Azure and Azure Data Factory.
  • Experience developing and maintaining data pipelines and Machine Learning pipelines.
  • Experience with Machine Learning models, particularly regression and classification models.
  • Practical experience with Generative AI solutions and LLM-based architectures.

Responsibilities

  • Develop, maintain, refactor, and optimize applications and data solutions using Python, PySpark, and Databricks.
  • Read, analyse, improve, and maintain existing and legacy code.
  • Develop and maintain data pipelines using Microsoft Azure and Azure Data Factory.
  • Develop and maintain data and Machine Learning pipelines.
  • Work with Azure cloud environments, including security, permissions, and data governance concepts.
  • Maintain and improve regression and classification Machine Learning models.
  • Evaluate model metrics and performance and support the lifecycle management of models in production.
  • Develop and maintain productive Generative AI solutions using GenAI models and APIs, mainly OpenAI.
  • Work with single-agent and multi-agent architectures.
  • Develop and maintain solutions based on LangChain and LangGraph.
  • Implement and maintain Retrieval-Augmented Generation (RAG) solutions.
  • Develop document indexing solutions and work with vector databases and document indexing techniques.
  • Implement observability and monitoring using Langfuse.
  • Develop and maintain REST APIs and integrations.
  • Work with relational databases, including PostgreSQL and SQL.
  • Perform data modelling and impact analysis.
  • Support application, pipeline, and model deployments across different environments (development, test, and production).
  • Apply DevOps practices and manage version control using GitHub.
  • Manage branches, pull requests, and releases.
  • Develop automated tests using pytest.
  • Analyse logs and troubleshoot technical issues.
  • Manage user permissions and data access.
  • Contribute to the management of the Technical Debt Backlog.
  • Maintain technical and functional documentation for applications and solutions.
  • Support infrastructure change requests.
  • Ensure adherence to software development best practices, code quality, automated testing, and continuous validation.
  • Work collaboratively with global teams and contribute to project delivery in an Agile environment.

Skills

Python
PySpark
Databricks
Azure
Azure Data Factory
GenAI
OpenAI
LangChain
LangGraph
REST APIs
PostgreSQL
SQL
GitHub
pytest
DevOps
JIRA
Confluence
SonarQube

Education

Bachelor's degree in Software Engineering, Computer Engineering, Computer Science, or a similar field

Tools

Azure Data Factory
PostgreSQL
GitHub

Job description

Decskill seeks a Tech Lead Data & AI Developer to design, build, and optimize data solutions using Python, PySpark, Databricks, and Azure Data Factory. You will craft data pipelines, ML workflows, and GenAI implementations within Azure, ensuring security and governance.

Expect multi-architecture work and an Agile global team collaboration across Lisbon and beyond. You will work on REST APIs, SQL databases like PostgreSQL, and model deployments, while upholding best practices, testing with

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