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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technology Lead | OpenSystem | Python - OpenSystem - **Company:** Ipolarity LLC - **Location:** Englewood, CO, United States (Remote available) - **Experience:** Expert - **Salary:** $213,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Cloud Computing, Github, Monitoring of Systems, Python (Programming Language), Machine Learning, Natural Language Processing, Tensorflow, Software Safety, Salesforce.Com, SAP (Applications), SQL Databases, Enterprise Software Applications, Feature Engineering, Pytorch, Large Language Models, Multi-Agent Systems, SOAPAPI, Generative AI, Fastapi, Web Filtering, Build Management, Scikit Learn, Uipath, Kubernetes, HuggingFace, Machine Learning Operations, Terraform, Software Version Control, Automation Anywhere, Docker - **Published:** August 10, 2026 - **Apply:** https://www.careerjet.com/jobad/us6852e8c09b7e0aa6ae514c4d9e47659d ## About the Role We are looking for a Senior AI / GenAI Engineer with strong experience in production-grade ML systems, Generative AI (LLMs, RAG, Agents), and enterprise automation. The ideal candidate will have hands-on expertise in deploying scalable AI systems, ensuring reliability, monitoring, and governance, and working across domains such as healthcare or enterprise IT operations., Core Technical Skills Strong Python development (FastAPI, ML libraries) ML frameworks: PyTorch / TensorFlow / Scikit-learn GenAI stack: OpenAI, Claude, LLaMA, Hugging Face RAG systems and vector databases (Pinecone, FAISS, etc.) MLOps & Systems MLflow, model registry, CI/CD pipelines Experiment tracking and automated testing Deployment patterns (batch + real-time inference) Data & APIs SQL, REST/SOAP APIs Experience with enterprise systems (SAP, Salesforce, etc. is a plus) Nice to Have Healthcare domain experience (HIPAA compliance, clinical or claims data) Experience with agentic workflows & human-in-the-loop systems Hands-on experience in cost optimization for LLM workloads RPA certifications (Automation Anywhere / UiPath) Project Code: SDIT Nebula Dev amp Support ## Description 1. GenAI & LLM Engineering Design and implement RAG pipelines using vector stores (Pinecone, FAISS, etc.) Build and deploy LLM-based applications using OpenAI, Claude, LLaMA, or similar Develop multi-agent systems (LangChain, LangGraph, CrewAI, Autogen) Optimize prompts, retrieval strategies, and model performance for production use 2. ML Engineering & Data Science Build and deploy ML models across: Classification, Regression, NLP, Time-series, and Anomaly Detection Perform EDA, feature engineering, and experiment design Implement A/B testing frameworks and performance evaluation pipelines 3. MLOps & Productionization Implement end-to-end ML lifecycle: Model training, testing, deployment, monitoring, and rollback Use tools like MLflow, CI/CD pipelines (GitHub Actions/Azure DevOps) Ensure model versioning, reproducibility, and governance Manage online & batch inference systems 4. Observability & Reliability Build monitoring systems for: Model drift Performance degradation Hallucination detection in LLMs Define incident response and rollback strategies Maintain dashboards and alerting frameworks 5. AI Safety & Compliance Implement AI guardrails: PII/PHI detection Content filtering Prompt injection defense Ensure compliance with regulatory standards (e.g., HIPAA) 6. Cloud & Infrastructure Deploy solutions on AWS, GCP, or Azure AWS Bedrock, SageMaker GCP Vertex AI Azure OpenAI / AI Foundry Build scalable infra using Docker, Kubernetes, Terraform 7. Enterprise Automation (RPA Integration) Design and support RPA workflows using Automation Anywhere / UiPath Integrate AI/ML models into automation pipelines Manage bot lifecycle, orchestration, and governance 8. Collaboration & Leadership Work with product, data, and engineering teams to deliver scalable solutions Mentor junior engineers and review technical designs Create documentation (PDDs, SDDs, architecture designs) ## Related Videos - [How E.On productionizes its AI model & Implementation of Secure Generative AI.](https://www.wearedevelopers.com/videos/623-how-e-on-productionizes-its-ai-model-implementation-of-secure-generative-ai) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [How to achieve web automation with UiPath](https://www.wearedevelopers.com/videos/310-how-to-achieve-web-automation-with-uipath) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) - [RPA crash course for .Net developers – intro into the world of RPA from the perspective of a .Net developer](https://www.wearedevelopers.com/videos/271-rpa-crash-course-for-net-developers-intro-into-the-world-of-rpa-from-the-perspective-of-a-net-developer) ## Related Articles - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)