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 ! Originally posted on Himalayas