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!
Must have skills & Qualifications:
8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure.
Strong expertise in AWS cloud-native ML stack, including: SageMaker(primary), EKS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent)
Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon.
Deep understanding of model lifecycle management (feature engineering->training → registry → deployment → monitoring).
Experience implementing or supporting LLMOps pipelines, including: prompt versioning, evaluation metrics, automation frameworks
Deep understanding of ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor).
Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
Experience working on Infrastructure as Code (IaC) tools and CI/CD pipelines
Experience with Kubernetes based development
Experience with feature engineering pipelines and Feature Store management.
Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, reproducibility.
Hands-on experience with AWS Bedrock and Agentcore service
Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana.
Strong foundation in Python and cloud-native development patterns.
Solid understanding of security best practices, IAM, secrets management, and artifact governance.
Good to have skills:
Experience with vector databases, RAG pipelines, or multi-agent AI systems.
Exposure to DevOps and infrastructure-as-code (Terraform, Helm, CDK).
Hands-on understanding of model drift detection, A/B testing, canary rollouts, and blue-green deployments.
Familiarity with Observability stacks (Prometheus, Grafana, CloudWatch, OpenTelemetry).
SQL and data transformation experience using Snowflake, Databricks, Spark.
Ability to translate business goals into scalable AI/ML platform designs.
Strong communication and cross-team collaboration skills.
Ability to guide engineering teams through technical uncertainty and design choices.
Key Responsibilities:
Architect and implement the MLOps strategy for the programme, ensuring alignment with the project proposal and delivery roadmap.
Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation.
Build container-oriented ML platforms (EKS-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, SageMaker, MLflow, Airflow, etc.).
Implement hybrid MLOps + LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems.
Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks.
Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.
Define and implement standards for model deployment, monitoring, governance, and automation to ensure production-grade reliability and scalability.
Collaborate with cross-functional teams — data engineering, platform, DevOps, and client stakeholders — to deliver production-ready ML solutions.
Ensure all solutions adhere to security, governance, and compliance expectations, particularly around handling cloud services, Kubernetes workloads, and MLOps tools.
Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms.
Mentor engineers and provide guidance on modern MLOps tools, platform capabilities, and best practices.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Skills Required
- 8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure
- Strong expertise in AWS cloud-native ML stack including SageMaker, EKS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent)
- Hands-on experience with at least one major MLOps toolset (MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon)
- Deep understanding of model lifecycle management (feature engineering, training, registry, deployment, monitoring)
- Experience implementing or supporting LLMOps pipelines including prompt versioning, evaluation metrics, and automation frameworks
- Deep understanding of ML lifecycle: data ingestion, feature engineering, training, evaluation, packaging, CI/CD, drift detection, monitoring, governance
- Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor)
- Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment
- Experience working on Infrastructure as Code (IaC) tools and CI/CD pipelines
- Experience with Kubernetes-based development (EKS-first, container-oriented ML platforms)
- Experience with feature engineering pipelines and Feature Store management
- Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, reproducibility
- Hands-on experience with AWS Bedrock and Agentcore service
- Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana
- Strong foundation in Python and cloud-native development patterns
- Solid understanding of security best practices, IAM, secrets management, and artifact governance
- Experience with vector databases, RAG pipelines, or multi-agent AI systems
- Exposure to DevOps and infrastructure-as-code (Terraform, Helm, CDK)
- Hands-on understanding of model drift detection, A/B testing, canary rollouts, and blue-green deployments
- Familiarity with observability stacks (Prometheus, Grafana, CloudWatch, OpenTelemetry)
- SQL and data transformation experience using Snowflake, Databricks, Spark
- Ability to translate business goals into scalable AI/ML platform designs
- Strong communication and cross-team collaboration skills
- Ability to guide engineering teams through technical uncertainty and design choices
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.
