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!
Job Role - Senior Machine Learning
Experience - 4-7 Years
Location - Mumbai/ Bangalore/ Trivandrum
We are seeking a highly skilled Senior Machine Learning Engineer specializing in conversational AI and agent systems. The ideal candidate will architect LLM-powered solutions, lead agent framework development, and collaborate with cross-functional teams to deliver enterprise-grade conversational AI systems on AWS cloud infrastructure.
Must have skills:
- Architect, develop, and deploy ML solutions at scale including traditional ML and LLM/conversational AI systems
- Lead end-to-end ML/AI lifecycle: data preparation, feature engineering, model development, validation, deployment, and monitoring
- Design production-ready agent frameworks, tool calling systems, and multi-agent coordination
- Implement MLOps best practices for deployment, monitoring, and optimization across ML and LLM systems
- Proven expertise in regression, decision trees, SVM, ensemble models, clustering, data preprocessing, feature selection, and statistical modeling
- Expert knowledge of prompt engineering, context optimization, agent reasoning patterns, RAG systems, vector databases, and semantic search
- Strong Python skills with ML libraries (scikit-learn, XGBoost, LightGBM) and agent frameworks (LangChain, CrewAI)
- Experience designing and implementing robust RESTful APIs for integrating ML models and conversational AI systems with enterprise applications and external services
- Experience with AWS Services : AWS Sagemaker, Bedrock, etc.
- Build scalable conversation analytics and AI system observability frameworks
- Collaborate with data scientists, data engineers, product managers, and stakeholders to translate business requirements into scalable ML/AI solutions
- Excellent problem-solving, communication, and stakeholder management skills
Good to Have Skills:
- Advanced AI Experience: Experience with Model Context Protocol (MCP) or similar agent communication standards
- Experience in customer support automation or contact center technologies
- Cloud AI certifications (AWS ML Specialty, Azure AI Engineer, Google Cloud ML Engineer)
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Skills Required
- 4-7 years of professional experience
- Experience architecting, developing, and deploying traditional machine learning and LLM or conversational AI solutions at scale
- Experience leading the end-to-end ML/AI lifecycle, including data preparation, feature engineering, model development, validation, deployment, and monitoring
- Experience designing production-ready agent frameworks, tool-calling systems, and multi-agent coordination
- Experience implementing MLOps practices for machine learning and LLM deployment, monitoring, and optimization
- Expertise in regression, decision trees, SVM, ensemble models, clustering, data preprocessing, feature selection, and statistical modeling
- Expertise in prompt engineering, context optimization, agent reasoning patterns, RAG systems, vector databases, and semantic search
- Strong Python skills with scikit-learn, XGBoost, and LightGBM
- Experience with LangChain and CrewAI or similar agent frameworks
- Experience designing and implementing RESTful APIs for ML and conversational AI integrations
- Experience with AWS services, including SageMaker and Bedrock
- Experience building conversation analytics and AI observability frameworks
- Strong problem-solving, communication, and stakeholder management skills
- Experience with Model Context Protocol or similar agent communication standards
- Experience in customer support automation or contact center technologies
- Cloud AI certification such as AWS ML Specialty, Azure AI Engineer, or Google Cloud ML Engineer
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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Wellbeing & Lifestyle Benefits — Wellbeing initiatives such as monthly meeting-free AMA-Zen Days, health check-ups, and wellness counseling are designed to reduce burnout and support day-to-day balance. Broader wellness programs reinforce both physical and mental health.
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Flexible Benefits — Remote/hybrid options with flexible working hours provide meaningful autonomy over where and when work gets done. Flexible leave constructs, including sabbaticals and special day leaves, add practical adaptability to the package.
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Parental & Family Support — Paid parental leave in the U.S., alongside maternity and childcare support, signals solid backing for families. These family-oriented policies integrate with a wider health and wellness focus.
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.
