Our team comprises of Data Scientists, ML/Full Stack Engineers, UI Developers, Product/ Program managers and QA testers working together to build products which offers unique analytical solutions to our clients.
What You'll Do
- Build, Refine and Use ML Engineering platforms and components.
- Scaling machine learning algorithms to work on massive data sets and strict SLAs.
- Build and orchestrate model pipelines including feature engineering, inferencing and continuous model training.
- Implement ML Ops including model KPI measurements, tracking, model drift & model feedback loop.
- Collaborate with client facing teams to understand business context at a high level and contribute in technical requirement gathering.
- Implement basic features aligning with technical requirements.
- Write production-ready code that is easily testable, understood by other developers and accounts for edge cases and errors.
- Ensure highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews.
- Write unit tests as well as higher level tests to handle expected edge cases and errors gracefully, as well as happy paths.
- Uses bug tracking, code review, version control and other tools to organize and deliver work.
- Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues and dependencies.
- Consistently contribute in researching & evaluating latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions.
What You'll Bring
- A master's or bachelor's degree in Computer Science or related field from a top university.
- 5+ years hands-on experience in ML development.
- Good fundamentals of machine learning
- Strong programming expertise in Python, PySpark/Scala.
- Expertise in crafting ML Models for high performance and scalability.
- Experience in implementing feature engineering, inferencing pipelines, and real time model predictions.
- Experience in ML Ops to measure and track model performance, experience working with MLFlow
- Experience with Spark or other distributed computing frameworks.
- Experience in ML platforms like Sage maker, Kubeflow.
- Experience with pipeline orchestration tools such Airflow.
- Experience in deploying models to cloud services like AWS, Azure, GCP, Azure ML.
- Expertise in SQL, SQL DB's.
- Knowledgeable of core CS concepts such as common data structures and algorithms.
- Collaborate well with teams with different backgrounds / expertise / functions.
Additional Skills
- Understanding of DevOps, CI / CD, data security, experience in designing on cloud platform;
- Experience in data engineering in Big Data systems
