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MLOps Architect (AWS)
MLOps Architect (AWS)
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MLOps Architect (AWS)

Nikita Sinha's profile picture
Posted by Nikita Sinha
8 - 14 yrs
Best in industry
Bengaluru (Bangalore), Mumbai
Skills
skill iconMachine Learning (ML)
skill iconAmazon Web Services (AWS)
SageMaker
AWS Bedrock
MLOps
LLMOps

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 modern 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.
  • 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 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. 
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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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About Quantiphi

Founded :
2013
Type :
Products & Services
Size :
1000-5000
Stage :
Profitable

About

Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Since its inception in 2013, Quantiphi has solved the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve accelerated and quantifiable business results.

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Tech stack

Google Cloud Platform (GCP)
skill iconNodeJS (Node.js)
skill iconPython
Artificial Intelligence (AI)
MLOps

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Bengaluru, Mumbai, and Trivandrum

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Shubham Vishwakarma

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I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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