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 Experience: 7-14 Years Location: Mumbai/Bangalore 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: 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. 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). • 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. Key Responsibilities: • 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!