MLOps Engineer

We are looking for MLOps Engineers who will own, develop, deploy, and operate tooling and services around MLOps. The candidate should be able to manage performant model training and tracking, ensure safe, stable, and scalable machine learning model deployments in both real-time and batch flows, with careful consideration of latency, reliability, and scalability. The role involves experiment tracking, validation, and hyperparameter optimization runs, along with model monitoring for downtime, latency, and drift detection. The candidate will be responsible for ensuring the scalability of the MLOps infrastructure and advancing its maturity to the next level. Additionally, they must build tools to democratize machine learning practices within TransGraph and work closely with product machine learning teams to understand their workflows, identify pain points, and provide effective solutions.

• Programming & Software Engineering: Python (Advanced), Git/GitLab, CI/CD, clean coding, testing, debugging, API development (Fast API, Flask, Django, REST, gRPC).
• Machine Learning & AI: End-to-end ML lifecycle, model training & deployment, ML algorithms, AI Agents & Chatbots (Lang Chain, Rasa), model diagnostics & optimization.
• MLOps & Data Engineering: MLflow, Kubeflow, DVC, W&B, Airflow, Prefect, PySpark/Spark, Pandas, Jenkins/GitLab CI/CD, Docker, Kubernetes, TensorFlow Serving.
• Cloud & Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, Helm, Terraform, Ansible, monitoring & logging (Prometheus, Grafana, ELK/EFK).

• Candidate with 3+ years across DevOps, MLOps, Machine Learning, and Data Engineering roles with strong exposure to production-grade systems.
• Incumbent must be skilled in Python, Git/GitLab, debugging, testing (unit/integration), and applying clean coding practices to build scalable solutions.
• Hands-on with the complete Machine Learning lifecycle data preparation, model development, deployment, monitoring with solid understanding of ML algorithms and dependencies.
• Experienced in designing and implementing MLOps Expertise architectures using MLflow, Kubeflow, Airflow, PySpark, and related frameworks for reproducibility and automation.
• Built and deployed AI Agents and conversational Chatbots leveraging modern frameworks and architectures.
• Proficient in diagnosing, deployment & Optimization and resolving issues around model performance, scalability, and deployment in production environments.
• Familiar with AWS/GCP/Azure, containerization (Docker, Kubernetes), CI/CD, and infrastructure automation. 8. Strong communicator and team player with an experimental, iterative approach driving best practices and cross-functional collaboration.

1. Proficiency with observability and monitoring tools: Prometheus, Grafana, Kibana, and
2. Experience with platform engineering and Kubernetes (K8s), Helm, and ArgoCD.
3. Knowledge of Apache Kafka or other streaming frameworks.
4. Exposure to frontend technologies and Java development.
5. Experience with feature stores for ML workflows.
6. Familiarity with GPU setup and management for accelerated deep learning workloads.