Technical Architect - ML

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__!_