Technical Architect - ML

## Technical Architect - MLApplylocations:
USA - Remote time type:
Full time posted on:
Posted Yesterday time left to apply:
End Date:
September 5, 2026 (30 days left to apply) job requisition :
JR11587

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:
Sage Maker(primary), EKS, Lambda, API Gateway, CI/CD (Code Build/Code Pipeline or equivalent)**
* ** Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, Sage Maker 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 Sage Maker** (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 Cloud Watch, Sage Maker 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 Dev Ops 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, Cloud Watch, Open Telemetry).**
* ** 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, Sage Maker, 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…