Software Solution Engineer
New
I
InterImageGenerative AI software
Workable workplace: remote; Workable locations: Arlington, Virginia, United StatesFull-TimeMiddle
Salary105,813 - 162,500 USD per year
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Job Details
- Experience
- 5+ years of software development experience; 2+ years developing Generative AI or Machine Learning applications.
- Required Skills
- DockerPythonFlaskKubernetesMicrosoft AzureFastAPICI/CDGenerative AI
Requirements
- 5+ years of software development experience.
- Strong Python development experience.
- Experience building APIs using FastAPI, Flask, or similar frameworks.
- Experience deploying cloud-native applications in Microsoft Azure.
- Familiarity with Docker and Kubernetes.
- Experience with Git, CI/CD pipelines, and DevSecOps practices.
- Strong understanding of software architecture and distributed systems.
- Preferred: Experience with Azure OpenAI Service and 2+ years developing Generative AI or Machine Learning applications.
- Preferred: Experience building RAG systems and working with LLMs such as GPT, Llama, Claude, or Mistral.
- Preferred: Experience with prompt engineering, AI workflow development, AI agents, and autonomous workflows.
- Preferred: Knowledge of orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, or LlamaIndex, and vector databases such as Pinecone, Milvus, pgvector, Azure AI Search, or Chroma.
- Preferred: Familiarity with MCP, model evaluation, observability, prompt optimization, and secure or classified AI deployments.
- Preferred: Familiarity with Terraform or Bicep and Azure Machine Learning, Azure AI Foundry, or Azure Cognitive Services.
Responsibilities
- Design and build Generative AI applications using commercial and open-source large language models.
- Develop proof-of-concepts and evolve them into production-ready software.
- Architect Retrieval-Augmented Generation solutions using vector databases and enterprise data sources.
- Build intelligent agents and AI workflows for reasoning, automation, and decision support.
- Develop REST APIs and backend services supporting AI applications.
- Deploy scalable AI solutions in Microsoft Azure using cloud-native architectures and Azure AI services.
- Build CI/CD pipelines supporting AI deployment and model iteration.
- Integrate AI capabilities into enterprise applications and mission systems.
- Evaluate emerging AI technologies and prototype new capabilities.
- Optimize model performance, latency, scalability, security, and cost; implement responsible AI practices, guardrails, and model evaluation.
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