MLOPs Engineer

Blackstraw Technologies; Chennai District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Blackstraw Technologies seeks an experienced MLOps Engineer to collaborate with engineers, product teams, and data scientists to push ML models into production. You will build scalable AI/ML workflows, RESTful Python services, and monitoring dashboards while driving reproducibility and performance across distributed systems.

You will design end-to-end pipelines for model training, deployment, and observability, and mentor peers to elevate engineering practices in a fast-paced, globally

Qualifications

• 3+ years of Mlops experience with strong Python programming (Bash familiarity).
• Solid understanding of ML lifecycle (training, validation, deployment, monitoring).
• Experience with ML frameworks: PyTorch, TensorFlow, scikit-learn.
• Model versioning/experiments with MLflow or Weights & Biases.
• Building and managing training and inference pipelines.
• Model packaging, reproducibility, rollback strategies.
• CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI.
• Hands-on with AWS/GCP/Azure cloud services.
• Managing storage, compute, and networking for ML workloads.
• Logging and observability with Prometheus, Grafana, ELK.

Responsibilities

• Create and maintain scalable infrastructure to deliver AI/ML processes with near real-time user responsiveness.
• Develop RESTful applications using Python.
• Design and implement training and deployment pipelines for ML models.
• Design dashboards to monitor systems, collect metrics, and create alerts.
• Perform feasibility studies and troubleshoot data/app issues.
• Contribute to architectural designs of large complexity with multiple components.
• Mentor engineers to promote good engineering practices.
• Collaborate with data scientists and end-users across cultures for compatibility and satisfaction.
• Use distributed computing to validate and process large data volumes.
• Evaluate technologies including open-source frameworks, libraries, and tools.
• Develop technical documentation with diagrams and manuals.

Skills

Python

Bash

ML lifecycle

PyTorch

TensorFlow

scikit-learn

MLflow

Weights & Biases

Training pipelines

CI/CD

AWS

GCP

Azure

Prometheus

Grafana

ELK

Distributed computing

Tools

PyTorch

TensorFlow

scikit-learn

MLflow

Weights & Biases

Prometheus

Grafana

ELK

GitHub Actions

Jenkins

GitLab CI

AWS

GCP

Azure

Job description

Role Description:

As a MLOps Engineer, you will collaborate with other Engineers, Development teams, Product Owners & Scrum Masters to realize critical business goals and be part of a team of smart, highly skilled technologists who are passionate about learning, data mining and prototyping cutting-edge technologies.

We develop RESTful API applications in Python and what we do is to help our research team to put ML models in production, so we work mostly in building Restful applications and training pipelines (mostly in AzureML), integrating inference codes, and providing tools and patterns for enhancing our MLOps cycle.

Responsibilities:

• Create and maintain a scalable infrastructure to deliver AI/ML processes, responding to the user requests in near real time.
• Develop RESTful Applications using Python.
• Design and implement the pipelines for training and deployment of ML models.
• Design dashboards to monitor a system, collect metrics, create alerts based on them and execute performance tests.
• Perform feasibility studies/analysis with a critical point of view and support & maintain (troubleshoot issues with data and applications).
• Contribute to architectural designs of large complexity and size, potentially involving several distinct software components.
• Mentoring other engineers fostering good engineering practices across the department.,
• Working closely with data scientists and a variety of end-users (across diverse cultures) to ensure technical compatibility and user satisfaction.
• Use distributed computing to validate and process large volumes of data to deliver insights.
• Evaluate technologies we can leverage, including open-source frameworks, libraries, and tools.
• Develop technical documentation for applications, including sequence diagrams, flowcharts and manuals.

Qualifications:

• 3+ years of applicable Mlops experience including strong programming in Python (and familiarity with Bash)
• Solid understanding of machine learning lifecycle (training, validation, deployment, monitoring)
• Experience with ML frameworks: PyTorch, TensorFlow, scikit-learn
• Model versioning, experiment tracking using MLflow / Weights & Biases
• Building and managing training & inference pipelines
• Model packaging, reproducibility, and rollback strategies
• CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI
• Hands‑on experience with AWS / GCP / Azure
• Managing storage, compute, and networking for ML workloads
• Logging & observability using Prometheus, Grafana, ELK