Senior Machine Learning Engineer - Data Analytics

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
• Good to have skills:
• AWS Bedrock Models
• Redshift and SQL
• Knowledge of Data Pipelines (GlueJobs)
• If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

Requirements

• 3-5 Years
• Location: Bangalore (Hybrid)
• 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
• 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
• AWS Bedrock Models
• Redshift and SQL
• Knowledge of Data Pipelines (GlueJobs)
• If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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