Quantiphi

Architect - Machine Learning (MLOps Specialist)

IN KA Bengaluru

Open 4 months, first seen by us 128 days ago

Pay: Pay not listed
Work setting: Not stated
Employment: Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting

Architect and implement the MLOps strategy for the EVOKE Phase-2 programme
Design and own enterprise ML and LLM pipelines with CI/CD automation
Build container-oriented ML platforms, EKS-first
Enable observability, drift detection, lineage tracking and auditability

From the employer’s posting

Architect and implement the MLOps strategy for the EVOKE Phase-2 programme

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)

Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.

Apply at Quantiphi

Posting record

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Open 4 monthsWe first saw this posting 128 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records: Live
First seen by us: Jun 29, 2026128 days ago
Recorded sightings: 178
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 128

Up longer than most. Similar jobs usually come down between day 6 and day 69.

From 4,296 similar postings that came down. Coming down is not always a hire.

Quantiphi is hiring

30 open jobs

Open jobs by the week we first saw them, last 12 weeks.

ML Engineer hiring has slowed
Takes jobs down at a usual pace
3% of open jobs were reposted

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
SQL
AWS
Databricks
Datadog
Grafana
Kubernetes
MLflow
SageMaker
Snowflake
Spark
Terraform
Airflow

Where the posting names them

- 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.

- 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).

- 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 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.

- 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.

- 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.

- 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.

- 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).

- 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.

- 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.

- 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.

- 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.

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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!

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