Statusneo - MLOps Engineer

Job Title : MLOps Engineer (6+ Years Experience)

Location : Gurgaon ( 5 Days - Wfo )

Employment Type :  Full-time

Experience Level :  Senior (6+ years)

We are seeking a seasoned MLOps Engineer with 6+ years of hands-on experience to join our growing AI / ML team. You will be responsible for designing, building, and maintaining scalable and reliable machine learning infrastructure and pipelines, enabling seamless model development, deployment, and monitoring. This role is ideal for someone who thrives at the intersection of data science, software engineering, and DevOps. It's  a competitor of HTC Global services, Brillio, Apexon.

Key Responsibilities

Design, implement, and maintain robust CI / CD pipelines for ML models and data workflows.

Automate and monitor the training, validation, deployment, and performance tracking of ML models.

Collaborate with data scientists, ML engineers, and DevOps teams to ensure production-grade ML systems.

Manage and optimize data pipelines using tools like Apache Airflow, Kubeflow, or MLflow.

Set up and maintain model versioning, experiment tracking, and reproducibility systems.

Build infrastructure for scalable model serving (batch and real-time) using Kubernetes, Docker, and cloud-native services.

Monitor model performance in production and implement re-training and drift-detection mechanisms.

Ensure compliance, security, and governance in all ML workflows.

Required Skills and Qualifications

Bachelors or Masters degree in Computer Science, Engineering, or a related field.

6+ years of experience in software development, DevOps, and / or machine learning infrastructure.

Strong experience with cloud platforms such as AWS, Azure, or GCP.

Proficiency in containerization (Docker) and orchestration (Kubernetes).

Solid knowledge of CI / CD tools like Jenkins, GitLab CI, CircleCI, etc.

Experience with ML lifecycle tools (MLflow, Kubeflow, SageMaker, Vertex AI, etc.).

Strong coding skills in Python and experience with ML libraries (scikit-learn, TensorFlow, PyTorch).

Familiarity with monitoring tools like Prometheus, Grafana, or ELK stack.

Experience with data pipeline tools (Airflow, Luigi, Prefect).

Preferred Qualifications

Experience with feature stores and metadata management.

Exposure to model interpretability and monitoring tools.

Experience in highly regulated industries (e.g., healthcare, finance) is a plus.

Certification in cloud platforms (e.g., AWS Certified Machine Learning Specialty).