Senior Software Engineer - ML
About the role
The Senior Software Engineer - ML role requires working with IT systems such as Datadog, New Relic, or ServiceNow, and involves responsibilities related to AI copilots, prompt engineering, and federated learning. The position demands strong problem-solving skills, effective communication for cross-functional teamwork, and a collaborative, self-starting attitude. Candidates should have a Bachelor’s or Master’s degree in a relevant field and 3-5 years of professional experience as a Data Scientist, with expertise in large-scale data environments, enterprise systems, and hands-on experience in Retrieval-Augmented Generation (RAG). Proficiency in Python or R, experience with AI Agent Frameworks, and familiarity with machine learning and deep learning models are essential. The role also involves working with APIs, databases, ETL processes, and cloud platforms like AWS, GCP, or Azure.
Must have
- Experience working with IT systems like Datadog, New Relic, or ServiceNow.
- Familiarity with AI copilots, prompt engineering, or federated learning.
- Basic DevOps knowledge
- Strong problem-solving abilities and a passion for innovative solutions.
- Excellent communication skills to work effectively in cross-functional teams.
- Self-starter with a collaborative mindset and an eagerness to learn and grow.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field.
- 3-5 years of professional experience as a Data Scientist, ideally with expertise in large-scale data environments and enterprise systems, along with hands-on experience in Retrieval-Augmented Generation (RAG).
- Experience with AI Agent Framework (AutoGen, LangChain, etc.)
- Proficiency in Python or R.
- Strong understanding of machine learning algorithms (e.g., classification, clustering, regression) and deep learning models (e.g., transformers, LSTMs).
- Familiarity with data retrieval from APIs and databases (SQL/NoSQL) and handling large datasets.
- Hands-on experience with data wrangling and ETL processes.
- Exposure to cloud platforms such as AWS, GCP, or Azure
- AI Agent framework
- Python
- SQL/NoSQL
Technologies
- AutoGen
- AWS
- Azure
- Data Science
- Datadog
- Deep Learning
- DevOps
- ETL
- GCP
- LangChain
- Machine Learning
- New Relic
- NoSQL
- Prompt Engineering
- Python
- RAG
- ServiceNow
- SQL
- Statistics
- Transformers
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