ML Ops Engineer

Job description

Our AI Delivery squad helps to transform data into insights using techniques such as machine learning, natural language processing, large language models, mathematical optimization, etc. It is looking for an experienced ML Ops Engineer:

Your profile

• You have a proven track record of hands-on experience in the area of AI/ ML/ Advanced Analytics, with special focus on deploying and maintaining AI/ ML models and services in production.
  • Keywords: AI/ ML application development, testing, serving, monitoring, troubleshooting.
  • You know how to ensure ML models are reproducible and interpretable.
  • You have already single-handedly packaged and deployed AI/ML services to production.
  • You know how to monitor and maintain AI/ML services post-deployment.

• You are proficient in Python
  • You have 5+ years of work experience with Python, and AI/ML standard libraries such as pandas, scikit-learn, xgboost
    • Nice-to-haves: 
      • Data processing libraries and frameworks (pydantic, pandera)
      • Web frameworks (such as FastAPI, Flask, ...)
      • CLI frameworks (Typer, Click, ...)
      • General MLOps tools and frameworks (MLFlow, Azure ML Studio, ...)
      • Version control tools for ML datasets and models (DVC, Azure ML Dataset, ...)
      • Monitoring libraries and solutions (such as NannyML, Evidently AI, ...)
      • Distributed processing libraries and frameworks (such as Ray, Dask, PySpark, ...)
      • Pipeline-building and orchestration libraries (such as Metaflow, ZenML, Kedro, Airflow, Dagster, ...)
      • General Python development tool (pytest, coverage, tox, mypy, black, ruff, uv, pip-compile, ...)

• You can write both object-oriented and functional code, and understand concepts such as (de)coupling, coherence, inheritance, composition.
• You make sure the code that you and your colleagues write is thoroughly tested (unit, integration, end-to-end, stress/performance).
• You love and regularly use data validation and type hints.
• You know how to turn a messy jupyter notebook into a production-grade piece of code.
  • Although we'll apply all possible preventive measure to prevent this from ever happening.
• You know how to package a python application or library for distribution
• You are a proficient GIT user, able to collaborate with multiple developers on multiple repositories, while following best practices related to branching, merging and code reviews.
• You have a good understanding of Machine Learning algorithms and their applications in NLP.
• You have work experience with at least one Cloud Provider, preferably Azure Cloud.
• You have experience with Unix/Linux command line tools and scripting (shell, bash):
  • VIP club membership if you have at least once ran `rm -rf` on production data.
• You possess the foundational Data Engineering skills, allowing you to interact with the Data Engineering team, and analyze and troubleshoot data pipelines if needed:
  • You could handle using SQL to extract, transform and load data (ETL/ELT).
  • Experience with the Hadoop ecosystem (Spark, Kafka, Hive, Impala…) is a plus.
    • Experience with the Cloudera distribution is an additional plus
• You understand the modern ML Ops framework and complexities it adds to DevOps.
  • You are able to identify the ML Ops maturity gaps and provide inputs for modernization efforts.

Non-technical

• You have strong verbal and written communication skills as well as good customer relationship skills to present complex concepts and/or the results of a use case to different audiences (from end users up to division management).
• You have experience of working in large, complex enterprises and have stoically accepted it as your fate.
• You are not allergic to legacy technology, yet are always on the lookout for modernization opportunities.
• You stay up-to-date with new tools, technologies and approaches within the domain.
• You are a well-integrated team player.
• You are able to estimate your short-term effort with reasonable accuracy and get the work done in the time frame you commit to.
• You successfully swim in the waters of Agile project management techniques (scrum boards, standups, demos, reviews).
• You stand to promote ML Ops and advocate for its usage and necessity across the organization.
• Must love mentoring and sharing knowledge.
• Must love dad jokes.

Your formal qualifications are the following:

• University degree in software engineering OR Data Science/Machine Learning/Data Engineering OR a related quantitative field, combined with strong IT skills.
• 5+ years of experience with Python
• 2+ years of experience of using DevOps/CI/CD practices.
• 2+ years of experience in deploying AI solutions to production