[Hiring] Mid ML Engineer @Cogniify

Role Description

We are seeking a Mid-Level AI/ML Engineer to design, build, and deploy machine learning models and end-to-end ML pipelines that drive business value. In this role, you will independently own the development of ML solutions from experimentation through production deployment, collaborate with cross-functional teams, and contribute to the maturity of our MLOps practices. The ideal candidate combines strong ML fundamentals with practical engineering skills and a growing ability to make independent technical decisions.

• Design, develop, and deploy machine learning models for classification, regression, NLP, computer vision, or recommendation use cases.
• Build and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, and serving.
• Implement MLOps practices including automated training pipelines, experiment tracking, model versioning, and reproducibility.
• Deploy models to production using containerization (Docker, Kubernetes) and cloud-native services.
• Monitor model performance in production, implement data drift detection, and manage model retraining workflows.
• Collaborate with data engineers, software engineers, and product teams to integrate ML solutions into applications and services.
• Optimize model performance for latency, throughput, and resource efficiency in production environments.
• Write production-quality code with proper testing, logging, error handling, and documentation.
• Participate in technical design discussions and contribute to architectural decisions for ML systems.
• Mentor junior engineers and contribute to team knowledge-sharing and best practices.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Data Science, or a related technical field.
• 3–5 years of professional experience in machine learning engineering, applied ML, or a closely related role.
• Strong proficiency in Python and hands-on experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience building and deploying ML pipelines in production environments.
• Working knowledge of MLOps tools and practices including MLflow, Kubeflow, Airflow, or similar orchestration frameworks.
• Experience with cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) for training, deployment, and serving.
• Proficiency in SQL and experience working with large-scale datasets.
• Experience with Docker, Kubernetes, and CI/CD pipelines for ML workflows.
• Solid understanding of model evaluation, hyperparameter tuning, and feature engineering techniques.
• Familiarity with REST APIs and microservices architecture for model serving.

Preferred Qualifications

• Experience with deep learning architectures such as Transformers, CNNs, or RNNs.
• Familiarity with feature stores (Feast, Tecton) and data versioning tools (DVC, LakeFS).
• Experience with model monitoring and observability tools (Evidently AI, WhyLabs, or Prometheus/Grafana).
• Exposure to distributed training frameworks and GPU-accelerated computing.
• Experience with A/B testing and experimentation frameworks for ML models.
• Knowledge of data engineering tools such as Spark, Kafka, or dbt.

Benefits

• Internet allowance
• Laptop
• PF
• Paid PTO
• Annual Bonus
• Graduity
• Yearly Team building experiences
• Mentorship and sponsorship opportunities
• Manager resources and support
• Life & accidental insurance for additional protection.