Senior Python AI Engineer — Cloud-Native ML

Compunnel, Inc.; Phoenix (AZ); On-site; USD 140,000 - 190,000; Full time

Job summary

Compunnel, Inc. seeks a Senior Python/AI Software Engineer to design, develop, and deploy secure, scalable AI solutions supporting Information Security, Governance, and Risk initiatives.

You will work with ML, Generative AI, and agentic AI, transforming business needs into production-ready applications. Responsibilities include building AI services with Flask/FastAPI, deploying on cloud platforms, and implementing end-to-end ML model lifecycles with observability and CI/CD pipelines.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, AI, or a related field is required.
• 8+ years of experience designing and implementing enterprise-scale software engineering and AI/ML solutions is required.
• Strong hands-on experience developing AI/ML, Generative AI, or data-driven applications is expected.
• Experience with AI/ML frameworks such as TensorFlow, PyTorch, scikit-learn, Flask, and FastAPI is required.
• Strong understanding of LLMs, NLP, embeddings, RAG, and prompt engineering is essential.
• Experience with agentic AI frameworks such as LangChain, AutoGen, CrewAI, or similar technologies.
• Experience developing and deploying AI applications using REST APIs.
• Experience with cloud platforms including AWS, Azure, and/or GCP.
• Experience with Docker, Kubernetes, and containerized deployment.
• Knowledge of vector databases such as pgVector or Milvus.
• Experience with MLOps tools like MLflow, Kubeflow, Argo Workflows.
• Experience with Kafka, Spark, NiFi, SQL, and NoSQL databases.
• Experience implementing CI/CD pipelines with Jenkins, GitLab CI, or XL Release.
• Familiarity with observability tools such as Prometheus, Elasticsearch, and Kibana.
• Strong understanding of software engineering best practices, system design, and scalable architecture.
• Excellent analytical, problem-solving, communication, and stakeholder collaboration skills.

Responsibilities

• Design, develop, and deploy AI/ML and Generative AI solutions using Python, LLMs, RAG, prompt engineering, and agentic AI frameworks.
• Translate business, security, governance, and risk requirements into scalable AI applications.
• Develop, train, evaluate, and optimize ML models for NLP, classification, clustering, anomaly detection, and predictive analytics.
• Support the complete AI model lifecycle including experimentation, evaluation, versioning, deployment, monitoring, and documentation.
• Build and maintain cloud-native AI pipelines using MLOps tools, APIs, Docker, Kubernetes, and modern deployment practices.
• Develop and integrate AI services using Flask, FastAPI, and RESTful APIs.
• Collaborate with Security, Risk, Engineering, Governance, and Data teams to deliver secure AI solutions.
• Implement observability, monitoring, and performance optimization for AI applications.
• Participate in code reviews, testing, troubleshooting, and continuous improvement initiatives.
• Stay current with emerging technologies in ML, Generative AI, MLOps, and responsible AI practices.

Skills

Analytical thinking

Problem-solving

Communication

Stakeholder collaboration

Software design

System architecture

Education

Bachelor's degree in Computer Science, Engineering, Data Science, AI or related field

Tools

TensorFlow

PyTorch

scikit-learn

Flask

FastAPI

LangChain

AutoGen

CrewAI

REST APIs

AWS

Azure

GCP

Docker

Kubernetes

pgVector

Milvus

MLflow

Kubeflow

Argo Workflows

Kafka

Spark

NiFi

SQL

NoSQL

Jenkins

GitLab CI

XL Release

Prometheus

Elasticsearch

Kibana

Job description

Compunnel, Inc. seeks a Senior Python/AI Software Engineer to design, develop, and deploy secure, scalable AI solutions supporting Information Security, Governance, and Risk initiatives.

You will work with ML, Generative AI, and agentic AI, transforming business needs into production-ready applications. Responsibilities include building AI services with Flask/FastAPI, deploying on cloud platforms, and implementing end-to-end ML model lifecycles with observability and CI/CD pipelines.