While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
Role : Senior Machine Learning Engineer - Data Analytics
Experience : 3-5 Years
Location : Bangalore (Hybrid)
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
Good to have skills:
AWS Bedrock Models
Redshift and SQL
ML Algos : Bagging and Boosting algorithms
Knowledge of Data Pipelines (GlueJobs)
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Skills Required
- Experience in Machine Learning with 3-4 years
- Proficiency in Python, including Pandas, Numpy, Matplotlib
- Knowledge of AWS services, especially SageMaker and Lambda
- Hands-on experience with OpenAI Framework and GenAI models
- Experience with data cleaning and feature engineering
Quantiphi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Quantiphi and has not been reviewed or approved by Quantiphi.
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Flexible Benefits — Hybrid and work-from-home options are commonly available and perceived as meaningful perks that increase overall package value. Flexibility by team and role often enhances day-to-day experience even when cash pay is not top-tier.
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Healthcare Strength — U.S. materials indicate medical coverage that includes dental and vision, and employee accounts align with having these plans in place. The presence of core health benefits contributes to a baseline of security across key locations.
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Parental & Family Support — Paid parental leave is available in the U.S., with examples citing generous leave lengths. Family-focused policies appear alongside other flexibility features.
Quantiphi Insights
What We Do
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to solve transformational problems at the heart of business. Quantiphi solves the toughest and complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed.
