Architect - Machine Learning (MLOps Specialist)

Quantiphi, Inc.; Mumbai; On-site; INR 3,000,000 - 4,500,000; Full time

Job summary

Quantiphi, Inc. is looking for an Architect - Machine Learning specializing in MLOps to join their team in Mumbai. The role involves architecting MLOps strategies, designing enterprise-grade ML/LLM pipelines, and collaborating with cross-functional teams to deliver production-ready ML solutions.

The ideal candidate will have extensive experience in ML/AI engineering, strong expertise with AWS cloud-native ML tools, and a solid foundation in Python.

Qualifications

• 8+ years in ML/AI engineering or MLOps.
• Hands-on with tools like MLflow or SageMaker.
• Strong experience in model lifecycle and CI/CD pipelines.
• Solid Python foundation and cloud-native patterns.

Responsibilities

• Architect MLOps strategy for EVOKE Phase-2 programme.
• Design ML/LLM pipelines for training, deployment, and monitoring.
• Collaborate with cross-functional teams for production readiness.
• Mentor engineers on MLOps tools and best practices.

Skills

AWS cloud-native ML stack

MLflow

Model lifecycle management

Python

CI/CD

SQL

Tools

SageMaker

Kubeflow

Docker

Terraform

Job description

## Architect - Machine Learning (MLOps Specialist)Applylocations: IN KA Bengaluru: IN MH Mumbai Eurekatime type: Full timeposted on: Posted Todayjob requisition id: JR8019While 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**!*