We are seeking a Senior Machine Learning Engineer to lead the design and deployment of advanced machine learning solutions that directly impact business outcomes. In this role, you will be responsible for full-lifecycle ML development — from data ingestion and model training to deployment and monitoring — in close collaboration with cross-functional teams.
This position is ideal for technically strong individuals who are ready to take ownership of complex ML initiatives, guide junior engineers, and contribute to shaping the company’s ML and MLOps strategy.
Architect, develop, and optimize ML models for real-time and batch systems.
Design and implement robust and scalable ML pipelines for feature engineering, training, and inference.
Collaborate with product managers, data scientists, and backend engineers to integrate ML into production systems.
Lead experimentation efforts including A/B testing, statistical validation, and model performance tuning.
Guide junior engineers through mentoring, code reviews, and best practice enforcement.
Ensure robust monitoring, logging, and alerting for deployed ML models.
Drive continuous improvement in model accuracy, latency, and infrastructure efficiency.
Education:
Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, Data Engineering, or related field.
Experience:
4–7 years of hands-on experience in designing, deploying, and maintaining machine learning systems at scale.
Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Deep understanding of supervised/unsupervised learning, feature engineering, and model evaluation techniques.
Experience deploying models via REST APIs, containerization (Docker), and orchestration (Kubernetes).
Proficiency with MLOps tools such as MLflow, TFX, DVC, or similar.
Familiarity with big data processing (e.g., Spark, Hive) and cloud environments (AWS, GCP, or Azure).
Strong grasp of software engineering principles — version control, testing, modularization, CI/CD.
Ability to communicate technical concepts clearly to both technical and non-technical stakeholders.
Experience with real-time ML systems or streaming data (e.g., Kafka, Flink).
Contributions to open-source projects, research publications, or technical blogs.
Exposure to ML fairness, explainability, or responsible AI practices.
Familiarity with data privacy and compliance considerations (e.g., GDPR, HIPAA).