quantiphi
Architect - Machine Learning (MLOps Specialist)
- Compensation
- Not disclosed
- Location
- IN KA Bengaluru
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 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.
Nice to have
- 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.
Full description
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.