ML Engineer

Implement, and optimize ML and AI models (e.g. classification/regression/clustering tasks, agent tuning etc).

• Experiment with state-of-the-art methods, frameworks, and architectures to improve performance and efficiency.
• Engineering & Deployment
• Build robust, scalable ML pipelines for training, validation, and inference.
• Deploy models to production (cloud or on-prem), ensuring reliability, latency, and scalability.
• Implement MLOps best practices (CI/CD, monitoring, retraining workflows, model registry).
• Data Engineering
• Partner with data engineering teams to source, clean, and transform large datasets.
• Ensure data quality, feature engineering, and real-time data integration.
• Collaboration
• Work closely with cross-functional stakeholders (data scientists, software engineers, product managers).
• Translate business requirements into ML solutions and communicate results effectively to technical and non-technical audiences.

• Master's degree in Computer Science, Machine Learning, Physics, or related field (PhD a plus).
• Technical Skills:
• Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with common agentic frameworks (LangChain, LangGraph preferred).
• Solid knowledge of algorithms, statistics, probability, and linear algebra.
• Experience with data pipelines and ETL (Spark, AWS Lambda, Glue ).
• Hands-on experience with cloud platforms (AWS preferred).
• Strong software engineering fundamentals (version control, testing, design patterns).
• Experience:
• 3+ years of professional experience in ML/AI engineering or related fields.
• Proven track record of deploying ML models into production at scale.
• Experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, etc.).
• Soft Skills:
• Excellent problem-solving, analytical, and communication skills.
• Ability to work independently and as part of a fast-paced, cross-functional team.