AI Application Engineer

Role & responsibilities

• Design, develop, and deploy enterprise AI applications using Large Language Models (LLMs).
• Build intelligent AI assistants and automation solutions for engineering and operational workflows.
• Develop Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources and internal documentation.
• Design and implement AI agents capable of performing multi-step workflows using APIs and external tools.
• Integrate AI applications with enterprise platforms, cloud services, databases, and REST APIs.
• Develop secure, scalable, production-ready AI solutions following enterprise security and governance standards.
• Evaluate and improve AI application quality through prompt engineering, retrieval optimization, model evaluation, and guardrails.
• Collaborate with cross-functional teams to identify and implement new AI use cases across cloud engineering, platform engineering, and security.
• Stay current with emerging AI technologies and recommend innovative solutions that deliver business value.

• Bachelor's degree in Computer Science, Information Technology, Engineering, or equivalent practical experience.
• 5+ years of software engineering experience.
• Strong proficiency in Python.
• Experience building REST APIs using FastAPI or similar frameworks.
• Strong understanding of software design patterns and distributed systems.
• Experience with Git, automated testing, and CI/CD pipelines.
• Experience developing containerized applications using Docker.
• Familiarity with Kubernetes and cloud-native application development.

• Large Language Models OpenAI, Azure OpenAI, Gemini, Claude, or similar.
• Prompt engineering.
• Retrieval-Augmented Generation (RAG).
• Embeddings and semantic search.
• Vector databases.
• AI agents / agentic AI.
• Function calling / tool calling.
• AI application evaluation and optimization.
• AI safety, guardrails, and hallucination reduction.

• Experience with one or more public cloud platforms:
  Microsoft Azure, Google Cloud Platform (GCP), or Amazon Web Services (AWS).
• Working knowledge of Identity and Access Management (IAM), cloud security best practices, REST APIs, enterprise authentication and authorization, infrastructure automation, and cloud governance.

Experience with one or more of the following:

• Frameworks:
  Lang Chain, Lang Graph, Llama Index, Semantic Kernel.
• Model Context Protocol (MCP).
• AI platforms:
  Azure AI Foundry, Google Vertex AI.
• Vector databases:
  Pinecone, Weaviate, Milvus, pgvector, or similar.
• Data stores:
  PostgreSQL, Redis.
• Infrastructure and orchestration:
  Terraform, Git Hub Actions, Airflow.
• CSPM tools such as Wiz, Prisma Cloud, or Orca.
• Enterprise chat platform integrations such as Microsoft Teams Bot Framework or Slack apps.
• Regulated or healthcare environment experience, including HIPAA awareness.