Note: The job is a remote job and is open to candidates in USA. Futuresoft Consulting Inc is seeking a hands-on AI Engineer to support the ongoing operation, modernization, and enhancement of AI-enabled applications. The role involves developing, maintaining, deploying, and supporting AI-powered applications and backend services while collaborating with various stakeholders.
Responsibilities
- Maintain, enhance, and extend existing AI-enabled applications and supporting services
- Develop application logic, backend services, and web APIs using Python and modern web frameworks such as FastAPI
- Build, test, and support applications powered by large language models, including OpenAI GPT, Anthropic Claude, Google Gemini, or similar platforms
- Compare and evaluate AI models based on task performance, accuracy, cost, latency, and reliability
- Configure, deploy, monitor, and maintain applications and services in cloud environments
- Deploy and support services using Microsoft Azure technologies such as Azure App Service, Azure Storage, Azure Key Vault, and related services
- Participate in requirements gathering, design discussions, and project planning sessions with business and technical stakeholders
- Prepare source code, debug issues, correct errors, and maintain software quality
- Use Git-based source control, branching, pull requests, and code review workflows
- Document technical procedures, system workflows, application processes, and deployment steps
- Apply secure development practices when working with confidential, regulated, or sensitive data
- Collaborate with client teams and the clients AI Lab to support AI application development and modernization efforts
- Participate in the evaluation of new AI tools, frameworks, models, and related technologies
Skills
- Minimum 3 years of Python development experience in a web application environment, including application logic and web APIs
- Minimum 2 years of experience building applications powered by large language models such as OpenAI GPT, Anthropic Claude, Google Gemini, or similar LLM platforms
- Experience building LLM-powered applications that use retrieval-augmented generation, document chunking, embedding generation, semantic or hybrid search, and vector stores
- Minimum 2 years of professional experience improving prompts and evaluating prompt outputs to improve response quality, reduce hallucinations, validate model outputs, and compare models based on accuracy, latency, cost, and reliability, including A/B testing or evaluating model performance for specific tasks
- Experience working with modern web frameworks such as FastAPI, Flask, or Django
- Experience deploying applications and supporting services in cloud environments, including hands-on experience with Microsoft Azure services such as Azure App Service, Azure Storage, Azure Key Vault, Azure Functions, or related services
- Experience using Git-based source control, branching, pull-request-based team workflow, code reviews, and CI/CD practices
- Strong understanding of secure development practices and proper handling of confidential or regulated data
- Experience collaborating directly with both technical and non-technical stakeholders, including business users, analysts, developers, cloud or platform teams, security or compliance teams, and leadership or program stakeholders
- Ability to take a user request from intake through design, development, testing, deployment, and production support with appropriate guidance, including clarifying requirements, explaining technical options, translating business needs into technical designs, communicating risks and tradeoffs, and explaining AI model behavior, limitations, and recommendations in clear language
- Experience discussing responsible AI, data security, model evaluation, hallucination risk, accuracy, cost, latency, and production reliability with non-technical stakeholders
- Experience using AI-assisted software development tools to write, review, debug, and improve code
- Located within the United States
- Available during Eastern Time business hours
- Available for occasional after-hours support for production issues, deployments, urgent troubleshooting, or system maintenance needs
- Experience with GitHub or Azure DevOps, especially Azure DevOps
- Experience with AI monitoring, observability, and governance, including tracking prompt performance, model behavior, response quality, drift, logging, and auditability
- Experience presenting technical designs, model comparisons, implementation recommendations, or production support updates to business or agency stakeholders
- Experience with prompt engineering, RAG, embeddings, vector databases, model evaluation, or AI application monitoring
Company Overview