Senior Machine Learning Engineer
At Mitratech, we are a team of technocrats focused on building world-class products that simplify operations in the Legal, Risk, Compliance, and HR functions of companies the world over. We are a close-knit, globally dispersed team that thrives in an ecosystem that supports individual excellence and takes pride in its diverse and inclusive work culture centered around great people practices, learning opportunities, and having fun! Our culture is the ideal blend of entrepreneurial spirit and enterprise investment, enabling the chance to move at a rapid pace with some of the most complex, leading-edge technologies available.
Given our continued growth, we always have room for more intellect, energy, and enthusiasm - join our global team and see why it’s so special to be a part of Mitratech!
Job Overview: As a Machine Learning Engineer at Mitratech, you will be assisting in the development of Artificial Intelligence products. The role will involve analyzing business requirements, understanding the data available, and building models that can solve problems in the legal industry.
Essential Duties & Responsibilities:
- Model Development: Design, implement, and deploy ML models (e.g., classification, NLP, recommendation, forecasting, computer vision) at scale.
- End-to-End Pipeline Ownership: Build, maintain, and optimize data ingestion, feature engineering, and model training pipelines using frameworks like TensorFlow, PyTorch, or Scikit-learn.
- ML Infrastructure: Assist and improve ML Ops processes (training, testing, deployment, monitoring) using tools such as Kubernetes, MLflow, Airflow, or SageMaker.
- Performance Optimization: Analyze model performance, conduct error analyses, and improve efficiency, latency, and accuracy.
- Collaboration: Partner with cross-functional teams to integrate ML-driven solutions into production systems.
- Leadership & Mentorship: Guide junior engineers and data scientists, set best practices, and help shape the ML engineering roadmap.
Requirements & Skills:
- Familiarity with vector databases, retrieval-augmented generation (RAG), or embedding pipelines.
- Knowledgeable in privacy-preserving ML, federated learning, or reinforcement learning.
- Experience with large-scale models (LLMs, foundation models) or multi-modal AI systems.
- Deep knowledge of ML algorithms, neural networks, and optimization techniques.
- Proficiency in Python and ML libraries (TensorFlow, PyTorch, XGBoost, Scikit-learn).
- Strong understanding of data structures, distributed systems, and cloud platforms (AWS, GCP, OCI, or Azure).
- Experience with ML Ops tools (Docker, Kubernetes, AgentCore, etc.).
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
- Experience with source code management tools such as Git
Education:
- Bachelor’s or Master’s in Computer Science, Machine Learning, Applied Mathematics, or related field
- 5+ years of professional experience in ML system design and deployment.
We are an equal opportunity employer that values diversity at all levels. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity, disability or veteran status.
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