Quantiphi
From Quantiphi's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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 : Architect - Machine Learning
8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure.
Strong expertise in AWS cloud-native ML stack, including: EKS (primary), ECS, 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 (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 (Training, Processing, Batch Transform, Pipelines, Feature Store, Model Registry, Model Monitor).
Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
Ability to build dynamic and versioned pipelines using SageMaker Pipelines, Step Functions, or Kubeflow.
Strong SQL and data transformation experience using Snowflake, Databricks, Spark, or EMR.
Experience with feature engineering pipelines and Feature Store management (SageMaker or Feast).
Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, reproducibility.
Hands-on experience with Bedrock, OpenAI, Anthropic, or Llama models.
Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana, or Datadog.
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).
Knowledge of Lakehouse (Delta/Iceberg/Hudi) architecture.
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.
Architect and implement the MLOps strategy for the EVOKE Phase-2 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, not limited to EVOKE, contributing architectural patterns, best practices, and reusable frameworks.
Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.
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!
Scores your resume against this job the way resume scanners do: the job's hard and soft skills, searchability, recruiter tips and formatting.
Create a free account to see your score. Your resume stays in this browser until then, and we never store it.
Already a member? Sign in