Machine Learning Engineer : 26-02588

Akraya, Inc.; Pleasanton (CA); Hybrid; USD 76,000 - 83,000; Full time

Job summary

Akraya, Inc. seeks an experienced Machine Learning Engineer in Pleasanton, CA for a 6+ month W2 contract. You will design, train, and deploy scalable ML models and data pipelines using Python, SQL, and Azure AI/ML services, with a focus on MLops and DevOps practices.

Responsibilities include building models for predictive analytics, NLP tasks, and deploying production-ready solutions. Hybrid/Remote work supported with PST hours.

Qualifications

• 5+ years of experience developing and deploying ML solutions.
• Strong expertise in supervised and unsupervised learning.
• Hands-on NLP and neural networks experience.
• Proficiency in Python, SQL, and R.
• Experience with Azure AI/ML services and cloud deployment.

Responsibilities

• Design, develop, train, and deploy machine learning models for predictive analytics.
• Build and optimize supervised and unsupervised learning models to solve business problems.
• Develop deep learning solutions using neural networks and NLP techniques.
• Create data pipelines for feature engineering, model training, validation, and inference.
• Deploy, monitor, and maintain ML models using Azure cloud services and MLOps.
• Collaborate with Data Scientists, Data Engineers, and stakeholders to translate requirements.
• Optimize model performance, accuracy, scalability, and reliability through experiments.
• Develop reusable Python libraries, APIs, and automation scripts for ML workflows.
• Implement CI/CD pipelines and DevOps practices for deployment and lifecycle management.

Skills

Machine Learning

Python & SQL

Deep Learning & NLP

Azure AI/ML

MLOps & DevOps

REST APIs

CI/CD pipelines

Docker & Kubernetes

Tools

TensorFlow

Keras

PyTorch

Azure Databricks

Azure OpenAI Services

Docker

Kubernetes

Job description

Primary Skills: Machine Learning (Expert), Python & SQL (Expert), Deep Learning & NLP (Advanced), Azure AI/ML (Advanced), MLOps & DevOps (Advanced)

Contract Type: W2 Only Duration: 6+ Months Location: Pleasanton, CA. Hybrid/Remote (Must Support PST Hours) Pay Range:$55 - $60 on W2

Job Summary: We are seeking an experienced Machine Learning Engineer to design, develop, deploy, and optimize scalable machine learning solutions for enterprise applications. The ideal candidate will have strong expertise in supervised and unsupervised learning, deep learning, natural language processing (NLP), and cloud-based AI platforms, with hands‑on experience deploying production-ready ML models using Azure and MLOps best practices.

• Design, develop, train, and deploy machine learning models for predictive analytics and intelligent automation.
• Build and optimize supervised and unsupervised learning models to solve complex business problems.
• Develop deep learning solutions using neural networks and NLP techniques.
• Create data pipelines for feature engineering, model training, validation, and inference.
• Deploy, monitor, and maintain ML models using Azure cloud services and MLOps best practices.
• Collaborate with Data Scientists, Data Engineers, and business stakeholders to translate requirements into scalable AI solutions.
• Optimize model performance, accuracy, scalability, and reliability through continuous experimentation and tuning.
• Develop reusable Python libraries, APIs, and automation scripts for ML workflows.
• Implement CI/CD pipelines and DevOps practices for model deployment, versioning, and lifecycle management.
• Document technical designs, model architectures, and deployment processes while following enterprise development standards.

• 5+ years of experience developing and deploying Machine Learning solutions.
• Strong expertise in Supervised and Unsupervised Machine Learning Algorithms.
• Hands‑on experience with Neural Networks and Natural Language Processing (NLP).
• Advanced programming skills in Python, SQL, and R.
• Experience with machine learning frameworks including TensorFlow, Keras, and PyTorch.
• Strong experience with Microsoft Azure AI/ML services and cloud-based model deployment.
• Hands‑on knowledge of DevOps and MLOps practices, including CI/CD pipelines and model lifecycle management.
• Experience with data preprocessing, feature engineering, model evaluation, and hyperparameter tuning.
• Strong analytical, problem‑solving, and communication skills.
• Ability to work independently while collaborating with cross‑functional technical teams.

• Experience with Azure Machine Learning, Azure Databricks, or Azure OpenAI Services.
• Familiarity with containerization technologies such as Docker and Kubernetes.
• Experience building REST APIs for ML model serving.
• Knowledge of Git, version control, and automated deployment pipelines.
• Experience working in Agile/Scrum environments and enterprise AI initiatives.