ML Engineer

Key Responsibilities

• Design and build scalable platforms and tools using advanced machine learning techniques including data parsing chunking and information extraction.
• Apply Bayesian programming methods to solve complex problems and enhance system capabilities.
• Implement and productionalize large language models (LLMs) using modern frameworks such as LangChain optimizing for performance and minimizing issues like hallucinations.
• Partner with stakeholders to identify impactful challenges and architect innovative ML solutions that align with both business priorities and technical feasibility.
• Explore and analyze datasets to identify actionable insights enabling the development of ML systems and new product features.
• Engineer effective features to maximize model accuracy and applicability in production.
• Build train and finetune machine learning models to meet key performance objectives.
• Evaluate models rigorously using relevant metrics to ensure they are ready for realworld deployment.
• Write clean productionready code for ML pipelines ensuring modularity scalability and maintainability.
• Collaborate with engineering teams to integrate ML models seamlessly with existing services through robust APIs.
• Deploy models in production environments leveraging best practices for scalability and reliability.
• Implement proactive monitoring systems to detect data drift model degradation and other performance issues ensuring longterm stability.
• Clearly communicate ML insights limitations and implications to nontechnical stakeholders fostering alignment with organizational goals.
• Act as a trusted advisor bridging the gap between technical capabilities and business needs.

Qualifications

Education

• Bachelors or Masters degree in Computer Science Statistics Data Science or a related technical field. Advanced degrees are a plus.

Experience

• 6 years of handson experience as a Machine Learning Engineer or Data Scientist with a proven track record of delivering impactful ML solutions in production environments.

Technical Expertise

• Machine Learning Fundamentals: Strong grasp of ML techniques and frameworks including training/validation workflows supervised/unsupervised learning feature engineering and optimization strategies.
• Programming & Libraries: Advanced proficiency in Python and handson experience with ML and data science libraries (e.g. Pandas NumPy scikitlearn PyTorch TensorFlow Transformers spaCy).
• Data Management & EDA: Expertise in wrangling cleaning and transforming raw data into structured formats optimized for machine learning applications.
• Model Evaluation: Deep understanding of performance metrics (e.g. F1Score Precision Recall) and the ability to contextualize and communicate model results effectively to both technical and nontechnical stakeholders.
• Production ML: Demonstrated experience in deploying and productionizing ML models designing pipelines and implementing monitoring systems to maintain longterm model performance and stability.
• Familiarity with cloud platforms such as AWS GCP or Azure and experience leveraging these environments for ML workflows.
• Exposure to containerization tools like Docker and orchestration platforms like Kubernetes is a strong plus.
• A creative and analytical thinker capable of tackling complex technical challenges with innovative scalable solutions.
• Passionate about learning and integrating new tools technologies and methodologies to stay at the forefront of ML advancements.
• Strong interpersonal skills with the ability to distill technical complexity into accessible insights for diverse audiences.
• A collaborative mindset with experience working crossfunctionally with engineering product and business teams.