Pune, Maharashtra, India
Information Technology
Full-Time
CoffeeBeans Consulting
Overview
Experience: 5 - 8 Years
Job Location: Gurgoan, Hyderabad (3 days work from office)
Purpose of the Job – A simple statement to identify clearly the objective of the job.
Key Responsibilities and Expected Deliverables– This details what actually needs to be done; the duties and expected outcomes.
Managing the lifecycle of machine learning models
Job Location: Gurgoan, Hyderabad (3 days work from office)
Purpose of the Job – A simple statement to identify clearly the objective of the job.
- The Senior Machine Learning Engineer is responsible for designing, implementing, and deploying scalable and efficient machine learning algorithms to solve complex business problems. The Machine Learning Engineer is also
- The position is highly technical and requires an ability to collaborate with multiple technical and non-technical profiles (data scientists, data engineers, data analysts, product owners, business experts), and actively take part in a large data
Key Responsibilities and Expected Deliverables– This details what actually needs to be done; the duties and expected outcomes.
Managing the lifecycle of machine learning models
- Develop and implement machine learning models to solve complex business problems.
- Ensure that models are accurate, efficient, reliable, and scalable.
- Deploy machine learning models to production environments, ensuring that models are integrated with software systems.
- Monitor machine learning models in production, ensuring that models are performing as expected and that any errors or performance issues are identified and resolved quickly.
- Maintain machine learning models over time. This includes updating models as new data becomes available, retraining models to improve performance, and retiring models that are no longer effective.
- Develop and implement policies and procedures for ensuring the ethical and responsible use of machine learning models. This includes addressing issues related to bias, fairness, transparency, and accountability.
Continuous Improvements
- Stay up to date with the latest developments in the field: read research papers, attend conferences, and participate in trainings to expand their knowledge and skills.
- Identify and evaluate new technologies and tools that can improve the efficiency and effectiveness of machine learning projects.
- Propose and implement optimizations for current machine learning workflows and systems.
- Proactively identify areas of improvement within the pipelines.
- Make sure that created code is compliant with our set of engineering standards.
Collaboration with other data experts (Data Engineers, Platform Engineers, and Data Analysts)
- Participate to pull requests reviews coming from other team members.
- Ask for review and comments when submitting their own work.
- Actively participate to the day-to-day life of the project (Agile rituals), the data science team (DS meeting) and the rest of the Global Engineering team
Education & Experience
- Engineering Master’s degree or PhD in Data Science, Statistics, Mathematics, or related fields
- 5 years+ experience in a Machine Learning Engineer role into large corporate organizations
- Experience of working with ML models in a cloud ecosystem
Statistics & Machine Learning
- Statistics: Strong understanding of statistical analysis and modelling techniques (e.g., regression analysis, hypothesis testing, time series analysis)
- Classical ML: Very strong knowledge in classical ML algorithms for regression & classification, supervised and unsupervised machine learning, both theoretical and practical (e.g. using scikit-learn, xgboost)
- ML niche: Expertise in at least one of the following ML specialisations: Timeseries forecasting / Natural Language Processing / Computer Vision
- Deep Learning: Good knowledge of Deep Learning fundamentals (CNN, RNN, transformer architecture, attention mechanism, …) and one of the deep learning frameworks (pytorch, tensorflow, keras)
- Generative AI: Good understanding of Generative AI specificities and previous experience in working with Large Language Models is a plus (e.g. with openai, langchain)
- Model strategy: Expertise in designing, implementing, and testing machine learning strategies.
- Model integration: Very strong skills in integrating a machine learning algorithm in a data science application in production.
- Model performance: Deep understanding of model performance evaluation metrics and existing libraries (e.g., scikit-learn, evidently)
- Model deployment: Experience in deploying and managing machine learning models in production either using specific cloud platform, model serving frameworks, or containerization.
- Model monitoring: Experience with model performance monitoring tools is a plus (Grafana, Prometheus)
Software Engineering
- Python: Very strong coding skills in Python including modularity, OOP, data & config manipulation frameworks (e.g., pandas, pydantic) etc.
- Python ecosystem: Strong knowledge of tooling in Python ecosystem such as dependency management tooling (venv, poetry), documentation frameworks (e.g. sphinx, mkdocs, jupyter-book), testing frameworks (unittest,
- Software engineering practices: Experience in putting in place good software engineering practices such as design patterns, testing (unit, integration), clean code, code formatting etc.
- Debugging: Ability to troubleshoot and debug issues within machine learning pipelines
- Data Visualization: Knowledge of data visualization tools such as plotly, seaborn, matplotlib, etc. to visualise, interpret and communicate the results of machine learning models to stakeholders. Basic knowledge of PowerBI is
- Data Cleaning: Experience with data cleaning and preprocessing techniques such as feature scaling, dimensionality reduction, and outlier detection (e.g. with pandas, scikit-learn).
- Data Science Experiments: Understanding of experimental design and A/B testing methodologies
- Databricks/Spark: Basic knowledge of PySpark for big data processing
- Databases: Basic knowledge of SQL to query data in internal systems
- Data Formats: Familiarity with different data storage formats such as Parquet and Delta
- Azure DevOps: Experience using a DevOps platform such as Azure DevOps for using Boards, Repositories, Pipelines
- Git: Experience working with code versioning (git), branch strategies, and collaborative work with pull requests. Proficient with the most basic git commands.
- CI / CD: Experience in implementing/maintaining pipelines for continuous integration (including execution of testing strategy) and continuous deployment is preferable.
- Azure Cloud: Previous experience with services like Azure Machine Learning Services and/or Azure Databricks on Azure is preferable.
Soft skills
- Strong analytical and problem-solving skills, with attention to detail
- Excellent verbal and written communication and pedagogical skills with technical and non-technical teams
- Excellent teamwork and collaboration skills
- Adaptability and reactivity to new technologies, tools, and techniques
- Fluent in English
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