ML Engineer Data Scientist
ML Engineer Data Scientist
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1 Vacancy
Job Description
Visa sponsorship eligibility: No
Job Description:
- Complete handson Experience in Python programming along with experience with popular AI/ML frameworks such as Tensorflow Pytorch scikitlearn Langchain and Llamaindex.
- Strong background in Machine learning models building and implementation.
- Handson experience in developing AI/ML/GenAI solutions using AWS services such as Sagemaker.
- Experience with search algorithms indexing techniques summarization and retrieval models for effective information retrieval tasks.
- Handson Experience with RAG architecture and its applications in natural language processing tasks.
- Good Exposure to Agentic / Multiagent framework.
- Handson Experience in endtoend development of machine learning and deep learning techniques like predictive modeling applied machine learning and natural language processing.
- Expertise in data engineering such as preprocessing and cleaning large datasets efficiently using Python PySpark and other manipulation tools like Pandas and NumPy. Experience with techniques such as data normalization feature engineering and data generation.
- Experience with cloud computing principles and experience in deploying scaling and monitoring AI/ML/GenAI solutions on cloud platforms like AWS.
- Deploy and monitor ML solutions using AWS services such as Lambda API gateway and ECS and monitor their performance using CloudWatch.
- Experience with Docker and containerization.
- Able to communicate complex technical concepts effectively to technical and nontechnical stakeholders and collaborate with crossfunctional teams.
Must Have:
- A Masters Degree in Computer Science and Engineering
- Minimum of 14 years of IT experience.
- Minimum of 7 experience as an ML engineer/data Scientist.
- Handson experience using Python and APIs like Flask/Django/fastAPI.
- Handson experience with tools such as Langchain llamaidnex and streamline.
- Handson experience with semistructured and unstructured data.
- Must have implemented a use case using LLMs.
- Must have implemented a use case using prompt engineering and finetuning of LLMs using LoRA/ PEFT.
- Must have implemented a use case using RAG architecture. Multiagent framework is an added advantage.
Employment Type
Full Time
