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Spydra
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MLOps Engineer

MLOps Engineer

Spydra

7 years

0 Lacs

Hyderabad Telangana India

Posted:3 months ago| Platform:

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Skills Required

ml data training model monitoring security automate engineering evaluation packaging deployment design latency kubernetes docker code integration drift drive logging metrics opentelemetry versioning audit iam management compliance autoscaling quantization retrieval collaboration service tooling devops typescript airflow github gitlab jenkins stack storage azure kafka victorops network vault tracking

Work Mode

On-site

Job Type

Full Time

Job Description

About the Role

We’re looking for an MLOps Engineer to build and operate reliable, secure, and scalable ML/LLM infrastructure—from data ingestion and training pipelines to model serving, monitoring, and continuous improvement. You’ll partner with Data Science, Platform, and Security teams to ship models to production with strong SLAs, observability, and cost control.

Responsibilities

Productionize models end-to-end:
automate data ingestion, feature engineering, training, evaluation, packaging, and deployment (batch & real-time).
Model serving & orchestration:
design/operate low-latency model endpoints and batch jobs using Kubernetes, Docker, job schedulers, and serving frameworks.
CI/CD for ML:
implement reproducible pipelines (code, data, features, models) with unit/integration tests, approvals, and canary/blue-green rollouts.
Monitoring & reliability:
build drift, performance, and data-quality monitors; set alerts and on-call runbooks; drive incident response and postmortems.
Observability:
instrument tracing/logging/metrics (e.g., OpenTelemetry, Prometheus, Grafana) across data flows and model requests.
Model registry & governance:
manage lineage, versioning, approvals, and audit trails; enforce security (IAM, secrets management) and compliance controls.
Cost & capacity management:
optimize GPU/CPU usage, autoscaling, caching, batching, quantization, and instance right-sizing.
LLM & RAG pipelines (nice if applicable):
stand up vector databases, retrieval flows, prompt/version management, guardrails, and evaluations.
Collaboration & enablement:
create templates, docs, and self-service tooling for data scientists and app teams.

Required Qualifications

3–7 years in MLOps/Platform/DevOps/SRE roles supporting ML in production.
Strong with
Python
and one of
Go/TypeScript/Bash
; proficiency in
Docker
and
Kubernetes
.
Experience building ML pipelines with tools like
Airflow/Prefect/Kedro/Flyte/Metaflow
.
CI/CD expertise (GitHub Actions/GitLab/Jenkins/Argo), including artifact/version management and automated testing.
Data stack: object storage (S3/GCS/Azure Blob), data warehouses/lakes, message queues/streams (Kafka/PubSub), and caching layers.
Monitoring/observability: Prometheus, Grafana, ELK/EFK, alerting (PagerDuty/VictorOps), tracing (OpenTelemetry/Jaeger).
Security fundamentals: IAM, network policies, secrets (Vault/SSM), image signing, SBOMs.
Solid understanding of ML lifecycle: data versioning, feature stores, experiment tracking, evaluation, and rollback.

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MLOps Engineer

Experience & Salary

Skills Required

Work Mode

Job Type

Job Description

Productionize models end-to-end:

Model serving & orchestration:

CI/CD for ML:

Monitoring & reliability:

Observability:

Model registry & governance:

Cost & capacity management:

LLM & RAG pipelines (nice if applicable):

Collaboration & enablement:

Python

Go/TypeScript/Bash

Docker

Kubernetes

Airflow/Prefect/Kedro/Flyte/Metaflow

More Jobs at Spydra

MLOps Engineer

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