Design, build, and deploy scalable machine learning and AI systems in production environments.
Collaborate with Product Managers, Data Scientists and Engineers to lead the implementation of models and integrate them into data pipelines and agentic applications.
Lead model performance monitoring, retraining workflows, and continuous improvement.
Lead the implementation of data preprocessing and feature engineering pipelines for ML use cases.
Lead to experimentation and testing of AI models to improve accuracy and performance.
Develop, maintain, and optimize scalable data pipelines for analytics and machine learning workloads.
Ensure data reliability, quality, and performance across data systems.
Implement and maintain data ingestion pipelines from various internal and external sources.
Build and improve internal tools that support data operations and data quality.
Monitor and troubleshoot data pipelines to ensure consistent and timely delivery.
Contribute to improving platform efficiency and scalability.
Participate in code reviews and follow best practices in data and ML engineering.
Document data pipelines, ML workflows, and system architecture.
Contribute to evolving data and AI engineering best practices.
Stay current with emerging tools, frameworks, and trends in AI, ML, and data engineering.

Requirements

4–7 years of experience in AI Engineering, Machine Learning Engineering, or Data Engineering.
Strong programming skills in Python, Java and SQL.
Proven experience designing and building production-grade ML systems and data pipelines.
Experience with Databricks, Apache Spark, or similar distributed data processing frameworks.
Strong understanding of machine learning lifecycle (training, deployment, monitoring, retraining).
Experience with cloud platforms (AWS, Azure, or GCP).
Solid knowledge of data architecture, data modeling, and data warehousing concepts.
Experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Familiarity with MLOps practices and tools (e.g., MLflow, Airflow, CI/CD pipelines).
Experience with version control (Git) and CICD development workflows.
Strong problem-solving, communication, and cross-functional collaboration skills.
Experience with LLMs, NLP, or generative AI applications.
Experience building end-to-end AI products or data-driven platforms.
Familiarity with real-time or streaming data pipelines.
Experience with cost optimization and performance tuning in Databricks.
Exposure to orchestration tools (Airflow, Dagster, etc.).
Experience mentoring or onboarding junior team members.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonJavaSQLmachine learning lifecycledata preprocessingfeature engineeringdata architecturedata modelingdata warehousingcost optimization

Soft Skills

problem-solvingcommunicationcross-functional collaborationmentoringleadership