Data Scientist
Data Scientist
AlgoLeap4-6 Years
- Posted 14 hours ago
- Be among the first 10 applicants
Job Description
Job Description
- 4+years of professional experience in AI, Machine Learning, Data Science, and/or Automation roles.
- Hands-on experience with developing and deploying AI solutions, with a foundational understanding of Generative AI concepts (e.g., LLMs, RAG architectures) and/or Agentic AI principles.
- Proficiency in Python programming.
- Familiarity with cloud-based AI/ML services (Azure, GCP, or AWS) and basic understanding of deployment processes.
- Basic understanding of data pipelines and MLOps concepts.
- Strong problem-solving skills and a keen interest in learning new technologies.
- Good communication and teamwork skills.
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Key Skills
About Company
Skills:
Machine Learning, Apis, Ms Azure, Neural Networks, Gcp, Dynamo Db, Python Programming, Prompt tuning, AgentCore, AWS Bedrock, Similarity search using vector databases, Prompt engineering, Simple Autonomous agents, Embedding generation, Tool-calling agent orchestration, Llm, Output evaluation, Agentic AI, Claude LLMs, Document ingestion, Workflow automation, Multi-step reasoning
Skills:
Machine Learning, Scipy, Natural Language Processing, Data Science, Numpy, Pandas, MLops, Docker, Python Programming, Predictive Modelling, Rest Apis, AWS, Similarity algorithms, Scikit-learn, Statistics, Fraud Detection, anomaly detection, Multilingual NLP, NLP text processing, ML model deployment, Entity matching
Skills:
Pyspark, Azure, Sql, Python, Cloud Computing, databrick, Sklearn ML
Skills:
graph databases , Data Manipulation, API design, Sql, Nosql, Typescript, Docker, Python, AWS, LangChain, vector databases, data lakes, Statistics, feature engineering, synthetic data generation
Skills:
Machine Learning, Artificial Intelligence, Scala, Text Mining, Deep Learning, Tensorflow, Nlp, Pytorch, MLops, Python, Model Evaluation Techniques, Interpretability Techniques, Transformer-based Models, Model Explainability, versioning, Vector Databases, Embeddings, Model Monitoring