Full Stack AI​/ML Engineer

Role Summary

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.

• Full Stack Development
  • Design and develop scalable, high-performance applications using .NET (C#), ASP.NET Core, and REST/Graph
    
    QL APIs.
  • Build responsive, intuitive frontend interfaces using React, Angular, or Blazor.
  • Architect microservices and event-driven systems using Amazon SQS, SNS, Kafka, or Rabbit
    
    MQ.
  • Integrate with relational (SQL Server, Postgre
    
    SQL, Amazon RDS) and No
    
    SQL (Mongo
    
    DB, Dynamo
    
    DB) databases.
  • Ensure application security, performance, and code quality through design reviews, unit testing, and CI/CD best practices.
• AI / ML Engineering
  • 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 Sage Maker, MLflow, or AWS Step Functions.
  • Implement RAG (Retrieval-Augmented Generation) architectures, vector databases (Pinecone, Weaviate, Amazon Open Search), 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.
• Cloud & Dev Ops
  • 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 Code Pipeline, Git Hub Actions, or Jenkins.
  • Implement infrastructure-as-code using Terraform or AWS Cloud Formation / CDK.
  • Ensure observability through logging, monitoring, and alerting using Amazon Cloud Watch, Datadog, or Grafana.
• Collaboration & Leadership
  • 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.

• 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 Tensor Flow.
• 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 (Sage Maker, Bedrock, Lambda, ECS/EKS, S3, RDS, Dynamo
  
  DB).
• Proficiency with SQL and experience with No
  
  SQL 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.

• Experience with MLOps frameworks (MLflow, Sage Maker Pipelines, Sage Maker Model Registry).
• Familiarity with vector databases and RAG-based architectures.
• Exposure to prompt engineering, fine-tuning, or LLM orchestration frameworks (Lang Chain, 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).