Senior Software Engineer (AI)

Job Title

Full Stack AI Engineer – AI/ML Implementation (Python, TensorFlow, Claude)

Location

Remote – USA

Employment Type

Contract

Experience Level

Mid-Level (4–6 Years)

Work Authorization

U.S. Citizenship Required (USC)

Education

Bachelor's degree or higher from an accredited U.S. college or university. A strong academic background from a reputable institution is preferred. U.S. educational background required.

Interview Requirements

• Strong technical competency relevant to the position is mandatory.
• Excellent verbal and written communication skills are required.
• Candidates must be able to clearly articulate technical concepts and participate effectively in client-facing discussions.
• Candidates must demonstrate professionalism and strong communication skills throughout the interview process.
• Successful completion of technical and communication assessments is required.

Job Summary

We are seeking a skilled Full Stack AI Engineer with 4–6 years of experience to design, develop, implement, and maintain production-ready AI and machine learning solutions.

The ideal candidate will have strong expertise in Python, TensorFlow, AI/ML development, Claude AI, Generative AI, and Azure cloud technologies.

This role involves working across the complete AI technology stack, including traditional machine learning, deep learning, large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI workflows.

The engineer will be responsible for translating business requirements into scalable, secure, and reliable AI solutions, from data preparation and model development through deployment, monitoring, and continuous improvement.

Candidates must be comfortable using AI-assisted development tools such as Claude Code and Cursor, alongside traditional software development practices, Git-based workflows, and CI/CD pipelines.

Key Responsibilities

AI/ML Development and Implementation

• Design, develop, train, test, and deploy machine learning and deep learning models.
• Develop solutions involving classification, regression, ranking, computer vision, natural language processing (NLP), and time-series analysis.
• Build and implement AI applications using Python, TensorFlow, PyTorch, scikit-learn, pandas, and NumPy.
• Evaluate model performance and continuously optimize accuracy, reliability, and efficiency.
• Translate business and scientific requirements into practical AI/ML solutions.

Generative AI, LLMs, and Claude Integration

• Design and implement LLM-powered applications using Anthropic Claude, Azure OpenAI, OpenAI, or equivalent platforms.
• Develop retrieval-augmented generation (RAG) solutions using embeddings, vector databases, and retrieval optimization techniques.
• Work with vector stores such as Azure AI Search and pgvector.
• Build agentic AI workflows involving tool integration, orchestration, and automated decision-making.
• Implement prompt engineering, fine-tuning, structured outputs, and model evaluation techniques.
• Establish AI guardrails, validation frameworks, and human-in-the-loop processes when necessary.

Full Stack AI Engineering and Development

• Develop scalable AI-powered applications and supporting backend services.
• Build and maintain data pipelines, feature engineering workflows, and AI service integrations.
• Use Claude Code and Cursor for AI-assisted software development while maintaining strong manual coding practices.
• Review, validate, and optimize AI-generated code to ensure maintainability, security, and performance.
• Collaborate with cross-functional teams to integrate AI capabilities into enterprise applications.

Azure Cloud, DevOps, and MLOps

• Design, deploy, and maintain AI/ML applications on Microsoft Azure.
• Work with Azure services such as Azure Machine Learning, Azure AI Services, Azure Kubernetes Service (AKS), Azure Functions, and App Service.
• Build and maintain automated CI/CD pipelines using Azure DevOps or GitHub Actions.
• Use Git for source control, branching strategies, pull requests, and code reviews.
• Develop containerized applications using Docker and Kubernetes.
• Implement infrastructure-as-code using Terraform or Azure Bicep.
• Apply MLOps best practices, including model versioning, automated retraining, performance monitoring, drift detection, and rollback strategies.

Monitoring, Security, and Collaboration

• Implement logging, tracing, monitoring, and evaluation frameworks for AI applications.
• Monitor model quality, application latency, operational performance, and cost.
• Apply security best practices related to data privacy, PII/PHI protection, prompt injection, and AI model risk.
• Collaborate with product managers, business stakeholders, engineering teams, and technical leadership.
• Participate in technical design discussions, architecture reviews, and code reviews.
• Provide guidance on AI-assisted development practices and LLM implementation best practices.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, or a related field.
• 4–6 years of professional software engineering experience, including at least 2 years of hands-on experience developing and deploying AI/ML solutions in production.
• Strong programming expertise in Python.
• Hands-on experience with TensorFlow and/or PyTorch, scikit-learn, pandas, and NumPy.
• Practical experience developing and deploying AI and machine learning models.
• Experience with Generative AI, LLM integration, and Anthropic Claude or similar AI platforms.
• Hands-on experience with Claude Code and Cursor for AI-assisted software development.
• Experience developing RAG applications, embeddings, and vector database integrations.
• Strong knowledge of REST APIs, service integration, and software engineering principles.
• Experience with Git, branching strategies, and pull-request-based development.
• Experience building and maintaining CI/CD pipelines using Azure DevOps or GitHub Actions.
• Hands-on experience deploying production applications on Microsoft Azure.
• Experience with Docker and cloud-based application deployment.
• Familiarity with MLOps practices, model evaluation, monitoring, and production support.
• Strong problem-solving, analytical, and debugging skills.
• Excellent written and verbal communication skills.
• Ability to communicate technical concepts to both technical and non-technical stakeholders.
• Proven ability to work independently and collaborate effectively in a fully remote environment.

Preferred Qualifications

• Experience with agentic AI frameworks and multi-agent workflows.
• Knowledge of LLM evaluation, fine-tuning, and prompt optimization.
• Experience with Azure AI Search, pgvector, or other vector databases.
• Experience with Kubernetes/AKS and infrastructure-as-code tools such as Terraform or Bicep.
• Knowledge of AI governance, responsible AI, data privacy, and security best practices.
• Experience developing enterprise-scale AI applications in regulated environments.
• Experience mentoring engineers and conducting technical code reviews.

Key Technical Skills

Programming

Python, REST APIs, Software Engineering

AI/ML Frameworks

TensorFlow, PyTorch, scikit-learn, pandas, NumPy

Generative AI

Claude, Anthropic, OpenAI, Azure OpenAI, LLMs, RAG, Agentic AI

AI Development Tools

Claude Code, Cursor

Cloud

Microsoft Azure, Azure AI Services, Azure ML, Azure Functions, AKS

DevOps/MLOps

Git, Azure DevOps, GitHub Actions, CI/CD, Docker, Kubernetes

Vector Databases

Azure AI Search, pgvector

Infrastructure

Terraform, Azure Bicep

Additional Skills

Prompt Engineering, Embeddings, Model Evaluation, Monitoring, AI Security

Work Arrangement

Fully Remote – United States

Employment Type

Contract

Experience Required

4–6 Years

“All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.”