Full stack AI Engineer

Tech3pillars Technologies; Virginia (MN); On-site; USD 140,000 - 190,000; Full time

Job summary

Tech3pillars Technologies in the United States is seeking an experienced AI/ML Engineer to design and implement advanced AI/ML solutions across business domains. You will build scalable pipelines, deploy models, and collaborate with stakeholders to translate requirements into measurable value.

The role emphasizes Python expertise, deep learning, and MLOps, with hands-on work on cloud platforms, data ecosystems, and AI copilots to craft practical, enterprise-grade AI applications.

Qualifications

• 5+ years of relevant experience required
• 10+ years of overall experience
• Must have skills: AI and Python

Responsibilities

• Design and develop AI/ML solutions using supervised, unsupervised, deep learning, NLP, time series forecasting, and anomaly detection techniques.
• Build Generative AI applications leveraging LLMs, prompt engineering, fine-tuning, RAG, and AI agent frameworks.
• Develop and maintain end-to-end AI pipelines, covering data ingestion, preprocessing, model training, deployment, monitoring, and continuous improvement.
• Demonstrate strong programming expertise in Python and SQL, with ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, SpaCy, and NLTK.
• Work with large-scale data ecosystems, including ETL processes, data lakes, data warehouses, streaming platforms, and tools like Spark, Databricks, or Microsoft Fabric.
• Implement MLOps best practices, including CI/CD pipelines, model governance, explainability, monitoring, Docker-based containerization, and Kubernetes orchestration.
• Deploy AI models through APIs and microservices, ensuring seamless integration with enterprise applications, systems, and cloud platforms.
• Utilize cloud-based AI services on Azure or AWS, including platforms such as Azure Machine Learning and Amazon SageMaker.
• Collaborate with business and technology stakeholders to translate business requirements into scalable AI solutions while tracking ROI and value realization.
• Contribute to AI innovation and best practices through reusable frameworks, AI copilots, semantic models, knowledge graphs, LangChain/Semantic Kernel orchestration, and synthetic data techniques

Skills

AI

Python

Tools

Java Springboot

Angular

Job description

Job Details

• Minimum years of experience required: 5+ years of relevant experience; 10+ years of overall experience
• Certification needed: No
• Must Have Skills: AI, Python
• Nice to Have Skills: Java Springboot, Angular

Detailed Job Description

1. Design and develop AI/ML solutions using supervised, unsupervised, deep learning, NLP, time series forecasting, and anomaly detection techniques to address business challenges.
2. Build Generative AI applications leveraging LLMs, prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), and AI agent frameworks.
3. Experience of working for banking domain
4. Develop and maintain end-to-end AI pipelines, covering data ingestion, preprocessing, model training, deployment, monitoring, and continuous improvement.
5. Demonstrate strong programming expertise in Python and SQL, with hands-on experience in ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, SpaCy, and NLTK.
6. Work with large-scale data ecosystems, including ETL processes, data lakes, data warehouses, streaming platforms, and tools like Spark, Databricks, or Microsoft Fabric.
7. Implement MLOps best practices, including CI/CD pipelines, model governance, explainability, monitoring, Docker-based containerization, and Kubernetes orchestration.
8. Deploy AI models through APIs and microservices, ensuring seamless integration with enterprise applications, systems, and cloud platforms.
9. Utilize cloud-based AI services on Azure or AWS, including platforms such as Azure Machine Learning and Amazon SageMaker.
10. Collaborate with business and technology stakeholders to translate business requirements into scalable AI solutions while tracking ROI and value realization.
11. Contribute to AI innovation and best practices through reusable frameworks, AI copilots, semantic models, knowledge graphs, LangChain/Semantic Kernel orchestration, and synthetic data techniques