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Quantiphi
Quantiphi
1,001 – 5,000 Employees
H1B Visa SponsorConsultingHealthcareLogistics
Quantiphi is an AI-first digital engineering company that helps organizations apply artificial intelligence, data, and cloud technologies to complex business challenges. Founded in 2013, the company combines consulting and engineering expertise to build scalable AI applications, develop data analytics capabilities, modernize cloud infrastructure, and deliver custom implementations. Its work supports organizations in sectors such as healthcare, finance, education, retail, and logistics, with technology partnerships including AWS, Google Cloud, and NVIDIA. Quantiphi’s teams focus on turning advanced technology into practical solutions for enterprise transformation.
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Senior Machine Learning Engineer at Quantiphi - Mumbai On-site

Lead the design and delivery of scalable machine learning, LLM, and conversational AI systems at Quantiphi. Build agent frameworks, MLOps capabilities, and AWS-integrated solutions.

INIndiaOn-siteFull-timeSeniorEnglish RequiredMachine Learning Engineer
AWSAzureCloudPythonScikit-Learn

Description

  • Design, develop, and deploy scalable machine learning solutions spanning traditional ML, LLMs, and conversational AI.
  • Own the full ML and AI lifecycle, from data preparation and feature engineering through model development, validation, deployment, and monitoring.
  • Create production-ready agent frameworks with tool calling and coordinated multi-agent workflows.
  • Apply MLOps practices across machine learning and LLM-based systems.
  • Develop reliable REST APIs that connect ML and conversational AI solutions with enterprise applications and external services.
  • Build scalable conversation analytics and observability capabilities for AI systems.
  • Partner with data scientists, data engineers, product managers, and stakeholders to turn business needs into scalable ML and AI solutions.
  • Architect LLM-powered solutions and lead agent-framework development on AWS infrastructure.

Requirements

  • 4–7 years of professional experience.
  • Proven ability to architect, develop, and deploy large-scale ML solutions, including traditional machine learning and LLM-based conversational AI.
  • Experience managing the complete ML and AI lifecycle, including data preparation, feature engineering, model development, validation, deployment, and monitoring.
  • Background designing production-grade agent frameworks, tool-calling workflows, and multi-agent coordination.
  • MLOps expertise covering deployment, monitoring, and optimization for ML and LLM systems.
  • Strong knowledge of regression, decision trees, SVMs, ensemble methods, clustering, data preprocessing, feature selection, and statistical modeling.
  • Advanced understanding of prompt engineering, context optimization, agent reasoning patterns, RAG, vector databases, and semantic search.
  • Strong Python skills with scikit-learn, XGBoost, LightGBM, LangChain, and CrewAI.
  • Experience building robust REST APIs for ML models and conversational AI applications.
  • Experience with AWS services such as SageMaker and Bedrock.
  • Experience developing scalable conversation analytics and AI observability frameworks.
  • Excellent problem-solving, communication, and stakeholder management abilities.
  • Experience with Model Context Protocol (MCP) or comparable agent communication standards is preferred.
  • Experience with customer support automation or contact center technologies is preferred.
  • Cloud AI certifications such as AWS ML Specialty, Azure AI Engineer, or Google Cloud ML Engineer are preferred.

Benefits

  • A workplace centered on transparency, diversity, integrity, learning, and career development.
  • An environment that supports innovation alongside professional and personal growth.

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