Senior Machine Learning Engineer

Model Development & Optimization

• Design and optimize machine learning models including deep learning architectures, LLMs, and specialized models (BERT-based classifiers)
• Implement distributed training workflows using PyTorch and other frameworks
• Fine-tune large language models and optimize inference performance using compilation tools (Neuron compiler, ONNX, vLLM)
• Optimize models for hardware targets (GPU, TPU, AWS Inferentia/Trainium)

Infrastructure Design & AI-Services Architecture

• Design AI-services and architectures for real-time streaming and offline batch optimization use-cases
• Lead ML infrastructure implementation including data ingestion pipelines, feature processing, model training, and serving environments
• Build scalable inference systems for real-time and batch predictions
• Deploy models across compute environments (EC2, EKS, SageMaker, specialized inference chips)

MLOps Platform & Pipeline Automation

• Implement and maintain MLOps platform including Feature Store, ML Observability, ML Governance, Training and Deployment pipelines
• Create automated workflows for model training, evaluation, and deployment using infrastructure-as-code
• Build MLOps tooling that abstracts complex engineering tasks for data science teams
• Implement CI/CD pipelines for model artifacts and infrastructure components

Performance & Cross-functional Partnership

• Monitor and optimize ML systems for performance, accuracy, latency, and cost
• Conduct performance profiling and implement observability solutions across the ML stack
• Partner with data engineering to ensure optimal data delivery format/cadence
• Collaborate with data architecture, governance, and security teams to meet required standards
• Provide technical guidance on modeling techniques and infrastructure best practices

Qualifications

Required Experience:

• Master's degree in Computer Science, Software Engineering, Machine Learning, or related fields
• 5+ years implementing AI solutions in cloud environments with focus on AI-services and MLOps
• 3+ years hands-on experience with ML model development and production infrastructure
• Proven track record delivering production ML systems in enterprise environments

Technical Competencies:

• ML & Deep Learning: PyTorch, TensorFlow, distributed training, LLM fine-tuning, transformer architectures, model optimization, ONNX, vLLM
• Cloud & Infrastructure: AWS services (EC2, EKS, S3, SageMaker, Inferentia/Trainium), Terraform/CloudFormation, Docker, Kubernetes
• Data & Processing: Python, SQL, PySpark, Apache Spark, Airflow, Kinesis, feature stores, model serving frameworks
• Development & Operations: Streaming/batch architectures at scale, DevOps, CI/CD (GitHub Actions, CodePipeline), monitoring (CloudWatch, Prometheus, MLflow)

Additional Requirements:

• Agile Methodology experience
• End-to-end ML systems experience from research to production
• Strong communication and collaboration skills
• Ability to work independently with minimal supervision
• Enterprise security and compliance experience

Preferred:

• Recommendation systems, NLP applications, or real-time inference systems experience.
• MLOps platform development and feature store implementations.

• Medical for full time employees
• Dental, and Vision Insurance
• Life Insurance, Short-Term Disability, Long-Term Disability, etc.