Alpha Business Solutions
Senior Machine Learning Engineer
Alpha Business Solutions - Chicago, Illinois, United States, 60290
Work at Alpha Business Solutions
Overview
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Overview
Our direct client a global American hospitality chain is seeking to hire for the role of Sr. Machine Learning Engineer.
Job Title: Sr. Machine Learning Engineer. Duration: 6 months plus | possible long-term. Location: Chicago IL 60606 (Hybrid 2 days/week) | Open to 100% Remote Pay rate: $90/HR - $105/HR (negotiable)
The Opportunity Seeks an experienced Machine Learning Engineer contractor to build algorithmic assets across Personalization, Generative AI, Forecasting, and Decision Science domains. This role combines deep technical modeling expertise with infrastructure engineering to design, build, and operate end-to-end ML/AI systems at scale. You'll implement foundational MLOps frameworks across the full product lifecycle including data ingestion, ML processing, and results delivery/activation. Working cross-functionally with data science, data engineering, and architecture teams, you'll serve as both solutions architect and hands-on implementation engineer. The Role 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. Benefits We offer a competitive compensation package that includes:
Medical for full time employees Dental, and Vision Insurance Life Insurance, Short-Term Disability, Long-Term Disability, etc. Please apply with your interest. You may also reach out to me directly at abaranwal@alphambe.com
Thank you, Ashu
Job Title: Sr. Machine Learning Engineer. Duration: 6 months plus | possible long-term. Location: Chicago IL 60606 (Hybrid 2 days/week) | Open to 100% Remote Pay rate: $90/HR - $105/HR (negotiable)
The Opportunity Seeks an experienced Machine Learning Engineer contractor to build algorithmic assets across Personalization, Generative AI, Forecasting, and Decision Science domains. This role combines deep technical modeling expertise with infrastructure engineering to design, build, and operate end-to-end ML/AI systems at scale. You'll implement foundational MLOps frameworks across the full product lifecycle including data ingestion, ML processing, and results delivery/activation. Working cross-functionally with data science, data engineering, and architecture teams, you'll serve as both solutions architect and hands-on implementation engineer. The Role 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. Benefits We offer a competitive compensation package that includes:
Medical for full time employees Dental, and Vision Insurance Life Insurance, Short-Term Disability, Long-Term Disability, etc. Please apply with your interest. You may also reach out to me directly at abaranwal@alphambe.com
Thank you, Ashu