This position is posted by Jobgether on behalf of a partner company. We are currently looking for an ML Ops Engineer in India.
We are seeking a highly skilled ML Ops Engineer to bridge the gap between machine learning development and operational deployment. In this role, you will design, implement, and maintain scalable ML pipelines while ensuring models are tested, monitored, and optimized in production environments. You will collaborate closely with data scientists, software engineers, and infrastructure teams to drive reliability, performance, and compliance in AI systems. Your work will have a direct impact on operational efficiency, clinical decision support, and quality outcomes. This role is ideal for proactive engineers passionate about AI / ML, cloud infrastructure, and deploying models that deliver measurable value.
Accountabilities
- Design, build, and maintain secure and scalable ML pipelines for model training, validation, deployment, and monitoring.
- Automate ML workflows using CI / CD pipelines and infrastructure-as-code tools.
- Collaborate with cloud infrastructure teams (e.g., Azure) to manage ML environments, ensuring compliance with governance and InfoSec policies.
- Develop and implement robust model testing frameworks, including performance, edge-case, and clinical validation.
- Build monitoring systems to detect model drift, overfitting, anomalies, and performance degradation in real time.
- Maintain detailed audit trails, logs, and metadata for all model versions, datasets, and configurations.
- Support model transparency, explainability, and governance using tools such as SHAP, LIME, or integrated APIs.
- Provide actionable insights to data scientists for continuous improvement of model accuracy, fairness, and efficiency.
- Advocate for best practices in MLOps, including reproducibility, version control, and ethical AI.
- Prepare documentation, model cards, and product guides for internal and external stakeholders.
Requirements
Bachelor’s Degree in Computer Science, Data Science, Machine Learning, or a related field.4+ years of experience in MLOps, DevOps, or ML engineering.Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Keras, Scikit-Learn, and XGBoost.Experience with containerization (Docker), orchestration (Kubernetes), and CI / CD pipelines.Strong analytical skills to interpret model performance data and optimize infrastructure usage.Experience deploying ML models in cloud environments (AWS, Azure, or GCS).Familiarity with source control (GitHub, GitHub Actions) and Agile development practices.Knowledge of healthcare datasets and privacy regulations is a plus.Preferred experience with MLOps platforms like MLflow, TFX, or Kubeflow.Exposure to feature flagging, rollback strategies, and production-level ML deployments.Benefits
Fully remote role with flexible work arrangements and regular mentor interactions.Opportunity to work on impactful AI / ML projects supporting operational and clinical decision-making.Access to cloud-based ML infrastructure and state-of-the-art development tools.Professional growth through collaboration with cross-functional teams and advanced ML workflows.Comprehensive documentation, training, and knowledge-sharing initiatives.Exposure to compliance, governance, and ethical AI best practices.Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.
When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.
🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
📊 It compares your profile to the job’s core requirements and past success factors to determine your match score.
🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.
🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.
The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.
Thank you for your interest!
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