Job Title: MLOps Engineer
Job Summary: We are seeking a highly skilled MLOps Engineer to build, automate, deploy, and manage machine learning infrastructure and production-grade AI/ML systems. The ideal candidate will bridge the gap between Data Science, Machine Learning, and DevOps teams by implementing scalable MLOps practices, automating model deployment pipelines, and ensuring the reliability, performance, and governance of machine learning solutions in production environments.
Key Responsibilities: - Design, develop, and maintain end-to-end MLOps pipelines for model training, deployment, monitoring, and retraining.
- Collaborate with Data Scientists and ML Engineers to operationalize machine learning models.
- Automate model deployment, testing, versioning, and monitoring processes.
- Build scalable infrastructure for machine learning workloads and AI applications.
- Implement CI/CD pipelines for machine learning workflows.
- Manage model lifecycle, governance, and reproducibility.
- Monitor model performance, drift, and data quality in production environments.
- Optimize infrastructure utilization, scalability, and operational efficiency.
- Ensure compliance with security, privacy, and regulatory requirements.
- Troubleshoot production issues and perform root cause analysis.
- Develop best practices for ML platform architecture and deployment standards.
- Support Generative AI and Large Language Model (LLM) deployment initiatives.
Required Skills: - Strong understanding of Machine Learning lifecycle and model deployment processes.
- Experience with cloud-based AI/ML services and infrastructure.
- Knowledge of DevOps, CI/CD, Infrastructure as Code, and automation practices.
- Experience monitoring and maintaining production machine learning systems.
- Strong problem-solving and analytical skills.
- Excellent communication and collaboration abilities.
Technical Skills: - Programming Languages: Python, SQL, Bash
- Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn
- MLOps Tools: MLflow, Kubeflow, Airflow, Metaflow, DVC
- Containerization: Docker, Kubernetes
- CI/CD Tools: Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps
- Cloud Platforms: AWS, Azure, Google Cloud Platform (GCP)
- Infrastructure as Code: Terraform, CloudFormation, Ansible
- Monitoring Tools: Prometheus, Grafana, Datadog, ELK Stack
- Data Processing: Apache Spark, Kafka, Hadoop
- Version Control: Git, GitHub, GitLab, Bitbucket
Qualifications: - Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, or a related field.
- Master's degree is a plus.
- Relevant certifications are preferred:
- AWS Certified Machine Learning - Specialty
- Google Professional Machine Learning Engineer
- Microsoft Azure AI Engineer Associate
- Certified Kubernetes Administrator (CKA)
Experience: - 4-8 years of experience in DevOps, Data Engineering, Machine Learning Engineering, or MLOps roles.
- Hands-on experience deploying machine learning models into production environments.
- Experience managing cloud-native AI/ML infrastructure.
- Experience with CI/CD automation and Kubernetes-based deployments.
Preferred Qualifications: - Experience with Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG).
- Knowledge of vector databases such as Pinecone, Weaviate, ChromaDB, or Milvus.
- Experience with AI model monitoring, drift detection, and observability platforms.
- Understanding of Responsible AI, model governance, and AI security practices.
- Experience with distributed training and GPU-based workloads.
Preferred Qualities: - Strong automation-first mindset.
- Excellent troubleshooting and performance optimization skills.
- Ability to work across Data Science, Engineering, and Operations teams.
- Strong ownership and accountability.
- Passion for AI innovation and cloud-native technologies.
Employment Type: Full-Time
Location: Remote / Hybrid / On-site
Nice to Have: - Experience with OpenAI, Anthropic Claude, Gemini, or other LLM platforms.
- Knowledge of LangChain, LlamaIndex, Haystack, and AI orchestration frameworks.
- Experience building enterprise AI platforms and self-service ML infrastructure.
- Familiarity with FinOps and cloud cost optimization for AI workloads.
- Experience supporting AI products in domains such as Healthcare, Finance, HR Tech, SaaS, or E-commerce.
