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Senior Machine Learning Engineer - Data Analytics
Quantiphi
Bengaluru, IND
Full-time
Senior · 3+ years experience
Hybrid
Discovered 3 weeks ago
PythonPandasNumPyMatplotlibOpenAI FrameworkLangGraph
Free
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PythonPandasNumPyMatplotlibOpenAI FrameworkLangGraph
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Role & Responsibilities
- Experimenting with range of models, evaluating model performance and model selection.
- Performing data cleaning, feature engineering, selection and evaluation.
- Implementing the data and model training pipelines on cloud using AWS services such as sagemaker, lambda functions, etc.
- Documentation for Model architecture and solutions
- Collaboration with cross-functional teams, including platform engineers, Machine learning engineers, software developers and business stakeholders, to ensure data solutions meet business needs.
- Adhering to project timelines
- Communicate with non-technical stakeholders to understand their data requirements and convey the benefits of data solutions, including migration strategies
Must have skills
- Machine Learning Engineer with 3–4 years of experience, based in Bangalore, with a requirement to work from the client’s office 2 days a week.
- Good exposure on Python (Pandas, Numpy, Matplotlib, Advance Python Syntax’s etc)
- Hands on experience on OpenAI Framework, required to develop AI applications.
- Handson experience in developing the RAG pipeline, LLM Gen AI models and Prompt Engineering.
- Handover experience on creating the MCP’s (Model Context Protocol).
- Exposure on Agentic frameworks like langgraph and langchain.
- Exposure to the Agentic framework (like AWS Bedrock Agentcore) is mandatory.
- Exposure on Data Analytics - Data Analytics, Advanced SQL and Amazon Redshift , AWS Glue , Amazon DynamoDB , Amazon Managed Streaming for Apache Kafka.
- Exposure on below AWS Services - Amazon Bedrock (AgentCore), Amazon SageMaker Studio , Amazon Elastic Container Registry , Amazon API Gateway , AWS Elastic Beanstalk , AWS Lambda , Amazon Elastic Container Service , Kubernetes.
- Hands-on GenAI Model Providers (example : OpenAI models, Anthropic models and Gemini Models).
- ML Algos : Bagging and Boosting algorithms
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