Objectives
Productionise ML solutions to deliver actionable insights from large-scale, multi-structured datasets, and make sure insights go all the way into the hands of users
Responsibilities
1) Design and implement MLOPS, i.e. set of tools and operating models used to deploy & maintain & monitor machine learning models in production reliably and efficiently
2) Support ML solutions go-live integration.
3) Act as a bridge between data scientists and IT & data engineers & data architects, especially during development & production architecture design & set-up
4) Design & build the technical architecture to deploy & maintain AI/ML in production
5) Refactor ML model training / testing code into CI-CD pipelines
6) Test and optimize machine learning models and algorithms before they go in production
7) Assist in monitoring and retraining models
Qualifications
1) Must Have:
a) Strong knowledge and experience in MLOPS (including TDD, CI/CD tools & methodologies)
b) Strong Software engineering skills, with reasonable knowledge of Python
c) Reasonable knowledge and experience in machine learning
d) Strong communication skills in English, both spoken and written
e) Familiar with Dockers, Kubernetes, API Design
f) Familiar with some DB/Cache systems such as SQL Server, MongoDB, Redis, etc.
g) Strong analytical, problem-solving and teamwork skills
h) 3+ years of relevant experience
i) Degree in computer science, math, statistics or related
2) Nice to have:
a) Good knowledge of Microsoft Azure stack (AMLS, Synapse, Data Factory, etc.)
b) Strong experience in Python web frameworks like Flask, Django, FastAPI, etc.
c) Strong knowledge of model monitoring & testing & ethical AI libraries such as evidentlyai, deepchecks, fairlearn, etc.
d) Good knowledge of Databricks
e) Knowledge of pySpark & SQL
f) Knowledge of RDBMS and 2+ NoSQL databases
What we are not looking for in this role
1) Programmers without a data track record
2) Data people without a programming track record