MLOps EngineerJob Title MLOps Engineer
Job Summary We are seeking a skilled MLOps Engineer to design, build, and maintain scalable machine learning infrastructure and deployment pipelines. The ideal candidate will bridge the gap between data science and software engineering by automating the end-to-end machine learning lifecycle, including model training, deployment, monitoring, versioning, and continuous integration/continuous deployment (CI/CD). You will work closely with data scientists, machine learning engineers, DevOps teams, and software developers to deliver reliable, production-ready AI solutions.
Key Responsibilities - Design, develop, and maintain scalable MLOps pipelines for machine learning model development and deployment.
- Automate model training, testing, validation, deployment, monitoring, and retraining workflows.
- Build and manage CI/CD pipelines for machine learning applications.
- Deploy and manage machine learning models on cloud platforms and Kubernetes environments.
- Implement model versioning, experiment tracking, and artifact management.
- Monitor model performance, data drift, concept drift, latency, and system health.
- Develop feature stores, model registries, and automated retraining pipelines.
- Collaborate with data scientists and ML engineers to optimize models for production environments.
- Ensure security, scalability, reliability, and compliance of ML infrastructure.
- Optimize infrastructure costs and resource utilization.
- Maintain technical documentation, deployment procedures, and operational best practices.
Required Qualifications - Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Software Engineering, or a related field.
- 3-6 years of experience in MLOps, DevOps, Machine Learning Engineering, or Cloud Engineering.
- Strong proficiency in Python.
- Experience with Linux, shell scripting, and Git.
- Hands-on experience with Docker and Kubernetes.
- Experience building CI/CD pipelines using GitHub Actions, GitLab CI/CD, Jenkins, or Azure DevOps.
- Knowledge of machine learning workflows and model deployment practices.
- Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
- Strong understanding of REST APIs and microservices architecture.
Preferred Qualifications - Experience with MLOps platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure Machine Learning, or Databricks.
- Familiarity with workflow orchestration tools such as Apache Airflow, Prefect, or Argo Workflows.
- Experience with feature stores, model registries, and experiment tracking.
- Knowledge of Infrastructure as Code (IaC) tools such as Terraform or CloudFormation.
- Experience with monitoring and observability tools such as Prometheus, Grafana, ELK Stack, or OpenTelemetry.
- Understanding of LLMOps, Generative AI deployment, Retrieval-Augmented Generation (RAG), and vector databases.
Technical Skills - Python
- Linux
- Shell Scripting
- Git
- Docker
- Kubernetes
- MLflow
- Kubeflow
- Apache Airflow
- Jenkins
- GitHub Actions
- GitLab CI/CD
- Terraform
- REST APIs
- AWS / Azure / Google Cloud
- SageMaker / Vertex AI / Azure Machine Learning
- Prometheus
- Grafana
- SQL
Soft Skills - Strong analytical and troubleshooting skills.
- Excellent problem-solving and automation mindset.
- Effective communication and collaboration skills.
- Ability to work in Agile and cross-functional teams.
- Attention to detail and commitment to operational excellence.
- Continuous learning mindset and passion for emerging AI technologies.
Nice to Have - Experience deploying Large Language Models (LLMs) and Generative AI applications.
- Familiarity with vector databases such as Pinecone, Milvus, Weaviate, or FAISS.
- Knowledge of distributed training frameworks such as Ray or DeepSpeed.
- Experience with GPU infrastructure and model optimization techniques.
- Contributions to open-source MLOps or AI infrastructure projects.
Benefits - Competitive salary and performance-based incentives.
- Comprehensive health and wellness benefits.
- Flexible or hybrid work arrangements.
- Learning, certification, and conference sponsorship opportunities.
- Access to modern cloud infrastructure, GPUs, and AI platforms.
- Opportunity to work on cutting-edge AI, machine learning, and cloud technologies in a collaborative environment.
