ML Ops

Position Overview

ML Ops Engineer

creative problem-solving mindset

Key Responsibilities

• Design, build, and maintain

  end-to-end ML Ops pipelines

  for deploying and scaling ML/AI models.
• Automate workflows for model training, deployment, monitoring, and retraining.
• Ensure

  scalability, reliability, and performance

  of ML systems in production.
• Work with ML Engineers and Data Scientists to bring models from

  experimentation to production

  .
• Manage

  model versioning, governance, monitoring, and logging

  .
• Implement CI/CD for ML workloads with Docker/Kubernetes and cloud platforms (Azure, AWS, or GCP).
• Support integration of models into client-facing applications and services.
• Apply ML Ops practices to

  LLMs, Vision, Speech-to-Text, and Image Recognition use cases

  .
• Stay up to date with

  emerging AI tools and frameworks

  (LangChain, Hugging Face, OpenAI APIs, etc.) and apply them to real-world problem solving.

Required Skills & Qualifications

• 2–3 years of professional experience in

  ML Ops / Data Engineering / AI Engineering

  .
• Strong programming skills in

  Python

  with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience with

  ML Ops tools

  (MLflow, Kubeflow, Airflow, DVC, or similar).
• Solid knowledge of

  CI/CD pipelines

  , Docker, Kubernetes, and cloud services (Azure ML, AWS Sagemaker, or GCP Vertex AI).
• Familiarity with

  LLMs, Vision, Speech-to-Text, and Image Recognition techniques

  — practical experience or strong conceptual knowledge.
• Understanding of

  prompt engineering, model fine-tuning, or transfer learning

  is a plus.
• Excellent problem-solving skills, creativity, and the ability to adapt quickly to new technologies.
• Strong communication and collaboration skills to work effectively with cross-functional teams and clients.

Preferred Skills

• Exposure to

  LangChain, Hugging Face Transformers, or RAG-based systems

  .
• Experience working with

  APIs and microservices

  to serve AI models.
• Knowledge of monitoring tools for deployed AI models (Prometheus, Grafana, EvidentlyAI).
• Familiarity with handling

  unstructured data

  (text, audio, images, video).

Education

• Bachelor’s or master’s degree in computer science

  , Data Science, AI/ML, or related field

  .

What We Look For

• A strong

  ownership mindset

  with the ability to deliver end-to-end solutions.
• Passion for solving

  real-world business challenges

  using AI.
• Professionals who can

  communicate complex concepts clearly

  to both technical and non-technical audiences.
• A

  creative thinker

  who stays ahead of trends in AI/ML and ML Ops practices.