Machine Learning Engineer
1 month ago
Experience : 4-7 Years
Location : Bangalore (WFO)
Looking for Immediate Joiners only.
Responsibilities :
- Build, Refine and Use ML Engineering platforms and components.
- ML Engineers with strong background in deploying and operationalizing ML models, Deep Learning Models (with focus on LLMs)
- 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.
Qualifications :
- A master's or bachelor's degree in Compute Science or related field from a top university.
- 4+ years' hands-on experience in ML development.
- Good fundamentals of machine learning and deep learning
- Experienced in backend API design, vector DB.
- 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, Airflow, Kubeflow, Tensorflow/Kears/Pytorch, or
other platforms.
- 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 / Skills :
- Understanding of DevOps, CI / CD, data security, experience in designing on cloud platform
- Experience in data engineering in Big Data systems
(ref:hirist.tech)
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