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# AIML Engineer

- **Company:** Diverse Lynx LLC
- **Location:** Minnetonka, MN, United States
- **Experience:** Expert
- **Salary:** $99,840.0 - $108,160.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Automation of Tests, Microsoft Azure, Code Review, Continuous Integration, Information Engineering, Information Leak Prevention, DevOps, Distributed Computing Environment, Monitoring of Systems, Python (Programming Language), Machine Learning, Pair Programming, Cloud Services, Tensorflow, Azure Machine Learning, Software Engineering, Management of Software Versions, Feature Engineering, Pytorch, Large Language Models, IT Architecture, Deep Learning, Generative AI, Pyspark, Scikit Learn, Kubernetes, Deployment Automation, Data Analytics, Machine Learning Operations, Api Design, Restful APIs, Artificial Intelligence Markup Language (AIML), Natural Language Understanding, Docker, Unsupervised Learning, Databricks
- **Published:** August 26, 2026
- **Apply:** https://www.careerjet.com/job/us7b21cb9bae387387b8b2743864e2d8d7/eaa

## About the Role

* 5+ years software engineering with 2+ years shipping ML models to production.
 * Strong Python skills and experience with ML frameworks (TensorFlow/PyTorch).
 * Experience with containers and orchestration (Docker/Kubernetes) and API development.
 * Understanding of ML system design (data leakage, training-serving skew, drift).
 * CI/CD and DevOps practices applied to ML workloads (MLOps).

Preferred / Nice to Have
Experience with feature stores, model registries, and model monitoring stacks.
GPU optimization and distributed training experience.
Experience with responsible AI toolkits and compliance requirements.
Core Skills (from POD sheet)
Python, TensorFlow, PyTorch, Docker, REST APIs
Role Descriptions: RESPONSITIBLIES1.Model Development DeploymentoDesign| develop| and deploy machine learning models and algorithms into production environments

 1. Apply deep learning technologies for tasks such as computer vision| NLP| NLU| ASR| and semantic understanding.
 2. AI Frameworks ToolsoWork with agentic AI frameworks like LangChain and LangGraph. oOptimize and quantize GenAI models| including those using RAG| Transformers| and VectorDBs3.Data Engineering AnalysisoHandle large-scale training and inference using platforms like Databricks and PySpark. oPerform statistical analysis| feature engineering| and dimensionality reduction (e.g.| PCA| SVD| UMAP| t-SNE). 4.Software Engineering IntegrationoBuild scalable AI platforms and integrate ML solutions into existing systems using REST APIs| Docker| and cloud services (Azure| AWS). oCollaborate with cross-functional teams to align AI architecture with business goals. 5.AI Governance StrategyoParticipate in AI governance processes including intake| proof-of-concept| and capital planningoBreak down large initiatives into manageable| cost-effective projects with clear KPIs. 6.Mentorship CollaborationoMentor junior AIML engineers and foster a collaborative| feedback-driven team cultureoEngage in code reviews|
pair programming| and community-based education initiatives
Essential Skills:
AIML Engineer

 * Required Skills 3 years of experience applying NLP (transformers| gpt| etc) in a production setting using large amounts of data
 * 2-3 years of experience applying LLMs (RAG| vector db| embeddings) to solve real world problems
 * 2-3 years of experience in Python using common data science libraries such as scikit-learn and Databricks Experience applying data science techniques in a commercial enterprise| including supervised| unsupervised learning| NLP| time-series forecasting and other statistical analyses
 * Experience building APIs for the models Strong written verbal communication skills including the ability to present detailed analyses to a broad audience Organized self-starter| with drive and commitment able to work with little supervision Strong analytical| quantitative| problem-solving| and critical thinking skills
 * Experience using vision and speech models in GenAI

## Description

* Translate data science prototypes into production-grade ML services and pipelines.
 * Build training and inference code with reproducibility, versioning, and automated testing.
 * Implement scalable model serving (online/offline), batching, and latency/throughput optimization.
 * Integrate model lifecycle tooling (tracking, registry, deployment automation, monitoring).
 * Collaborate with Data Engineering on feature pipelines and data contracts.
 * Own production health: drift detection, performance regression, rollback strategies, and incident response., Sager Electronics is looking for a Power Sales Engineer (PSE) to drive growth within the assigned region by expanding existing customer relationships and developing new business op…
 + 2 hours ago


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