Senior Technical Consultant

Hi Connections,

Urgent - Hiring for below role

About the Role:

We are seeking a seasoned and highly skilled MLOps Engineer to join our growing team. The ideal candidate will have extensive hands-on experience with deploying, monitoring, and retraining machine learning models in production environments. You will be responsible for building and maintaining robust and scalable MLOps pipelines using tools like MLflow, Apache Airflow, Kubernetes, and Databricks or Azure ML. A strong understanding of infrastructure-as-code using Terraform is essential. You will play a key role in operationalizing AI/ML systems and ensuring high performance, availability, and automation across the ML lifecycle.

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Key Responsibilities:

· Design and implement scalable MLOps pipelines for model training, validation, deployment, and monitoring.

· Operationalize machine learning models using MLflow, Airflow, and containerized deployments via Kubernetes.

· Automate and manage ML workflows across cloud platforms such as Azure ML or Databricks.

· Develop infrastructure using Terraform for consistent and repeatable deployments.

· Trace API calls to LLMs, Azure OCR and Paradigm

· Implement performance monitoring, alerting, and logging for deployed models using custom and third-party tools.

· Automate model retraining and continuous deployment pipelines based on data drift and model performance metrics.

· Ensure traceability, reproducibility, and auditability of ML experiments and deployments.

· Collaborate with Data Scientists, ML Engineers, and DevOps teams to streamline ML workflows.

· Apply CI/CD practices and version control to the entire ML lifecycle.

· Ensure secure, reliable, and compliant deployment of models in production environments.

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Required Qualifications:

· 5+ years of experience in MLOps, DevOps, or ML engineering roles, with a focus on production ML systems.

· Proven experience deploying machine learning models using MLflow and workflow orchestration with Apache Airflow.

· Hands-on experience with Kubernetes for container orchestration in ML deployments.

· Proficiency with Databricks and/or Azure ML, including model training and deployment capabilities.

· Solid understanding and practical experience with Terraform for infrastructure-as-code.

· Experience automating model monitoring and retraining processes based on data and model drift.

· Knowledge of CI/CD tools and principles applied to ML systems.

· Familiarity with monitoring tools and observability stacks (e.g., Prometheus, Grafana, Azure Monitor).

· Strong scripting skills in Python

· Deep understanding of ML lifecycle challenges including model versioning, rollback, and scaling.

· Excellent communication skills and ability to collaborate across technical and non-technical teams.

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Nice to Have:

· Experience with Azure DevOps or GitHub Actions for ML CI/CD.

· Exposure to model performance optimization and A/B testing in production environments.

· Familiarity with feature stores and online inference frameworks.

· Knowledge of data governance and ML compliance frameworks.

· Experience with ML libraries like scikit-learn, PyTorch, or TensorFlow.

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Education:

· Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.