IN KA Bengaluru, India · hace 7 meses
Experimentarás y evaluarás modelos de aprendizaje automático, construirás pipelines de datos y entrenamiento en AWS y desarrollarás soluciones de GenAI y agénticas, como sistemas RAG. También colaborarás con equipos multifuncionales y documentarás arquitecturas de modelos.
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! Thank you for your interest in Quantiphi! We are a team that dreams together and collaborates to ensure success. We are currently looking for exceptional individuals who can join us and contribute to a fun, diverse and hybrid work culture. As a part of the Quantiphi family, you will get ample opportunities to learn, grow and interact with colleagues from varied experience and backgrounds around the globe. If this excites you, explore our current openings and apply to any job roles that suit and interest you. Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of business. We solve the toughest and most complex business problems with the latest and cutting-edge technologies and to make this happen, we have a vibrant, diverse and talented set of professionals we proudly refer to as the Quantiphi family. Our culture is built on transparency, diversity, integrity, learning and growth. We are committed to provide our teams with an environment that helps them to learn, grow and flourish in their professional as well as personal lives.
Publicada el 5 abr 2026
