it 02 software developer artificial intelligence software solutions

Heres a breakdown of what IT 02 Software Developer - Artificial Intelligence Software Solutions typically means, based on common industry and government (e.g., IRS, DHS, VA) job classification systems. The "IT 02" Classification What it is: This is a pay band or job grade level (often used in the U.S. federal government or large contractors). Equivalent: It generally maps to GS-9 to GS-11 or a junior-to-mid-level role. Experience Level: Usually requires a Bachelors degree (in CS, IT, or related field) plus 12 years of experience, or a Masters degree. Autonomy: You work under general supervision, but are expected to solve complex problems, write code, and contribute to design. Core Responsibilities for "AI Software Solutions" As a Software Developer focused on AI, your job is not to research new AI (thats a Data Scientist or Research Scientist). Instead, you build, integrate, and deploy AI functionality into real applications. Common daily tasks: API Integration: Connecting to OpenAI, Google Vertex AI, AWS Bedrock, or Azure Cognitive Services. Prompt Engineering: Designing and optimizing prompts for LLMs (e.g., GPT-4, Claude, Llama) for specific use cases. Data Pipelines: Building ETL pipelines to clean and prepare data for ML models. RAG Implementation: Developing Retrieval-Augmented Generation systems (e.g., chat with your documents). Model Serving: Deploying trained models (PyTorch, TensorFlow) as REST APIs using Flask/FastAPI or containerized with Docker/Kubernetes. Evaluation & Testing: Writing unit tests for model outputs (e.g., checking for drift, bias, or hallucination). Frontend/Backend: Occasionally building the UI (React, Streamlit) or backend logic that orchestrates AI calls. Required Tech Stack (Typical for this role) Category Tools/Languages : : Languages Python (primary), JavaScript/TypeScript (for web), Java/C# (for enterprise). AI/ML Libraries LangChain, LlamaIndex, Hugging Face Transformers, TensorFlow/PyTorch (basic). Databases PostgreSQL, Pinecone/Weaviate (vector DBs), MongoDB. Cloud AWS (SageMaker, Lambda), Azure (AI Services), GCP (Vertex AI). DevOps/MLOps Docker, Git, CI/CD pipelines, MLflow (for tracking experiments). Key Differences from a "Regular" Developer Uncertainty: AI outputs are probabilistic, not deterministic. You must build error handling for "best guess" results. Data Focus: You will spend significant time understanding data structure, quality, and labeling. Ethics/Bias: You must be aware of model bias, toxicity, and safety guardrails. Monitoring: You need to track model performance over time (accuracy, latency, cost). Example Job Tasks for an IT 02 AI Developer "Develop and maintain a conversational chatbot using RAG architecture to assist customers with product documentation." "Write Python scripts to fine-tune a small language model on internal company data." "Containerize a computer vision inference service and deploy it to AWS ECS." "Create a system that automatically summarizes meeting transcripts using LLM APIs." In short: If you are an IT 02 AI Software Developer, you are the bridge between a raw AI model and a usable product. You focus on engineering (not mathematics), integration (not research), and deployment (not experimentation). Would you like a sample resume bullet point or a skill roadmap for this specific role?