We are seeking an experienced MLOps Architect who can drive end-to-end implementation of the proposal being prepared for the initiative and also contribute broadly across other enterprise AI/ML programs. This role demands a strong architectural mindset, hands-on technical depth, and the ability to design scalable, cloud-native machine learning operations across traditional ML and up-to-date LLM workflows.

The ideal candidate will bring experience with SageMaker-based MLOps pipelines, evaluation of equivalent tooling stacks, hybrid MLOps/LLMOps automation, CI/CD orchestration, governance, and production-grade scalability patterns.

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), 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 (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.
- Strong SQL and data transformation experience using Snowflake, Databricks, Spark.
- 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 Bedrock, OpenAI, Anthropic, or Llama models.
- Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana.
- Robust 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 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.
- 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.

Skills:- Machine Learning (ML), Amazon Web Services (AWS), SageMaker, AWS Bedrock, MLOps and LLMOps

📌 MLOps Architect (AWS) (Bengaluru)
🏢 Quantiphi
📍 Bengaluru