Full Stack AI/ML Engineer (.NET)

American IT Systems•New York City, NY

4d•Onsite

About The Position

We are looking for a skilled Full Stack AI/ML Engineer with strong .NET development experience to design, build, and deploy intelligent applications that combine robust backend engineering with applied machine learning capabilities. The ideal candidate bridges the gap between traditional enterprise software development and modern AI/ML systems, delivering end-to-end solutions from data pipelines to production-ready user interfaces.

Requirements

8+ years of software engineering experience with at least 4+ years focused on .NET (C# / ASP.NET Core).
Hands-on experience building and deploying ML models using Python-based frameworks such as Scikit-learn, PyTorch, or TensorFlow.
Practical experience integrating LLMs and generative AI capabilities into production applications.
Strong proficiency with frontend frameworks — React, Angular, or Blazor.
Solid understanding of RESTful API design, microservices architecture, and event-driven systems.
Strong experience with AWS cloud services (SageMaker, Bedrock, Lambda, ECS/EKS, S3, RDS, DynamoDB).
Proficiency with SQL and experience with NoSQL data stores.
Hands-on experience with Git, CI/CD pipelines, and Agile development methodologies.
Strong problem-solving skills and ability to work independently in a fast-paced environment.

Nice To Haves

Experience with MLOps frameworks (MLflow, SageMaker Pipelines, SageMaker Model Registry).
Familiarity with vector databases and RAG-based architectures.
Exposure to prompt engineering, fine-tuning, or LLM orchestration frameworks (LangChain, Semantic Kernel, AWS Bedrock Agents).
Knowledge of financial services or banking domain.
AWS certifications (Solutions Architect, ML Specialty, or Developer Associate) are a plus.
Experience with containerization (Docker, Kubernetes / Amazon EKS).

Responsibilities

Design and develop scalable, high-performance applications using .NET (C#), ASP.NET Core, and REST/GraphQL APIs.
Build responsive, intuitive frontend interfaces using React, Angular, or Blazor.
Architect microservices and event-driven systems using Amazon SQS, SNS, Kafka, or RabbitMQ.
Integrate with relational (SQL Server, PostgreSQL, Amazon RDS) and NoSQL (MongoDB, DynamoDB) databases.
Ensure application security, performance, and code quality through design reviews, unit testing, and CI/CD best practices.
Design, develop, and deploy machine learning models for use cases such as classification, regression, anomaly detection, recommendation, and NLP.
Integrate large language models (LLMs) — including Amazon Bedrock, OpenAI, or open-source alternatives — into enterprise applications via APIs and prompt engineering.
Build and maintain ML pipelines using Amazon SageMaker, MLflow, or AWS Step Functions.
Implement RAG (Retrieval-Augmented Generation) architectures, vector databases (Pinecone, Weaviate, Amazon OpenSearch), and embedding models.
Monitor model performance in production, manage model drift, and implement retraining workflows.
Collaborate with data engineers to ensure high-quality feature engineering and data availability.
Deploy and manage applications on Amazon Web Services (AWS) — including EC2, ECS/EKS, Lambda, S3, and API Gateway.
Build and maintain CI/CD pipelines using AWS CodePipeline, GitHub Actions, or Jenkins.
Implement infrastructure-as-code using Terraform or AWS CloudFormation / CDK.
Ensure observability through logging, monitoring, and alerting using Amazon CloudWatch, Datadog, or Grafana.
Work closely with product managers, data scientists, and UX designers to translate business requirements into technical solutions.
Mentor junior engineers and conduct code and architecture reviews.
Participate in Agile ceremonies (sprint planning, retrospectives, stand-ups).
Document technical designs, APIs, and AI model specifications clearly.