Role & Responsibilities:

• 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.

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.