AL ML Engineer

Job Summary

We are seeking a seasoned AI/ML Engineer to build, deploy, and scale machine learning solutions that power our next-generation data products. The candidate will collaborate with data scientists, product managers, and software engineers to translate research into production-ready services that deliver measurable business impact.

Key Responsibilities

• Design, implement, and maintain end‑to‑end machine learning pipelines, from data ingestion to model serving, ensuring high throughput and low latency.
• Prototype and evaluate new algorithms, including deep learning, graph neural networks, and reinforcement learning models, for business‑critical use cases.
• Automate model training, hyperparameter tuning, and continuous integration/continuous deployment (CI/CD) workflows using tools such as MLflow, Kubeflow, or Airflow.
• Monitor deployed models for drift, performance degradation, and bias, and orchestrate retraining and rollback procedures as needed.
• Partner with cross‑functional teams to define feature requirements, translate them into technical specifications, and present findings to stakeholders.

Required Qualifications

• Minimum 5 years of professional experience building production ML systems in a fast‑paced tech environment.
• Strong proficiency in Python, with extensive experience in PyTorch or TensorFlow for deep learning and scikit‑learn for classical ML.
• Hands‑on experience with containerization (Docker) and orchestration (Kubernetes) for scalable model deployment.
• Solid understanding of data engineering concepts: SQL, distributed data processing (Spark/Databricks), and data versioning.

Preferred Qualifications

• Experience implementing MLOps practices on cloud platforms such as AWS SageMaker, GCP Vertex AI, or Azure ML.
• Background in natural language processing or computer vision, with a portfolio of deployed projects.
• Familiarity with reinforcement learning frameworks (e.g., RLlib, OpenAI Gym) and their production use cases.

Required Skills

• Python programming and advanced libraries (NumPy, Pandas, PyTorch/TensorFlow, scikit‑learn)
• Containerization (Docker) and orchestration (Kubernetes)
• CI/CD pipelines and version control (Git, GitHub Actions, GitLab CI)
• Model monitoring and observability (Prometheus, Grafana, Seldon)
• SQL and NoSQL databases (PostgreSQL, MongoDB)
• Distributed data processing (Apache Spark, Databricks)
• Cloud services (AWS, GCP, or Azure) and serverless computing
• Experience with experiment tracking tools (MLflow, Weights & Biases)

Education

Master’s degree in Computer Science, Electrical Engineering, Data Science, or a closely related field.

Benefits

• Competitive salary with annual performance bonuses
• Comprehensive health, dental, and vision insurance plans
• 401(k) retirement plan with company match
• Generous paid time off, flexible remote work options, and a continuous learning stipend