ML Engineering Lead at Saris AI tackling multi - modal AI systems in banking. Drive technical direction and build high - performing teams in an early - stage startup environment.
About the role
- Machine Learning Engineer designing, building, and operationalizing AI/ML solutions for mission-critical applications. Collaborating with data engineering teams to support production-grade ML systems in a hybrid work environment.
Responsibilities
- Collaborate with data scientists and subject matter experts to develop machine learning models using curated datasets.
- Conduct experiments, prototypes, and proof-of-concepts to validate and refine model performance.
- Build scalable, reusable training pipelines using Databricks notebooks and MLflow.
- Implement and optimize Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and AI agent architectures for enterprise use cases.
- Operationalize models using robust CI/CD workflows.
- Deploy ML solutions via MLflow, AWS SageMaker, or custom APIs.
- Monitor production performance for accuracy, drift, and latency; manage retraining cycles and model governance.
- Partner with Data Engineering to align ML pipelines with the Bronze, Silver, and Gold layers of a Medallion Architecture.
- Engineer high-quality features and maintain training and inference pipelines.
- Utilize AWS services such as S3, EC2, Lambda, SageMaker, and Step Functions for scalable ML workloads.
- Document ML artifacts, processes, and performance outcomes clearly and comprehensively.
- Collaborate within agile teams, support project ceremonies, and maintain stakeholder communication.
- Mentor junior team members and share best practices.
Requirements
- 5+ years of experience in ML Engineering or Applied Machine Learning.
- Strong Python programming skills.
- Hands-on experience with major ML frameworks (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow).
- Proficiency with Databricks, MLflow, and PySpark.
- Solid understanding of the end-to-end model lifecycle and MLOps best practices.
- Experience with AWS-based data infrastructure and DevOps workflows.
- Proven ability to productionize ML models and integrate them into business systems.
- Strong understanding of mathematics and statistics relevant to ML and AI.
- Experience with supervised, unsupervised, and deep learning techniques.
- Solid background in software engineering principles and best practices.
- Hands-on experience with training frameworks such as TensorFlow, PyTorch, or Hugging Face.
- Practical experience building and deploying LLMs, RAGs, and AI agent systems.
- Demonstrated expertise with Databricks for data engineering and ML pipeline development.
- Excellent communication and teamwork skills.
Benefits
- Medical
- Dental
- Vision
- 401k with match
- Flexible Spending Account
- Paid Time Off (PTO)—including vacation and holiday pay
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
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