- Company Name
- Plentific
- Job Title
- AI Software Architect
- Job Description
-
Job Title: AI Software Architect
Role Summary: Design, develop, and deploy end‑to‑end AI‑powered features within a Python‑based cloud platform, integrating LLMs, RAG pipelines, and intelligent agents into production workflows. Work cross‑functionally with product, data science, and engineering teams to translate business requirements into scalable, secure, and maintainable AI services.
Expactations:
- Deliver high‑quality, production‑grade AI solutions that meet defined business objectives.
- Ensure rapid iteration and continuous improvement of prototypes into fully deployed services.
- Uphold best security and compliance practices for sensitive data and AI model exposure.
- Produce clear, maintainable documentation and facilitate knowledge transfer across teams.
- Stay current with emerging AI frameworks, deployment techniques, and cloud‑native MLOps tools.
Key Responsibilities:
- Build and integrate AI features (chat assistants, automation agents, predictive analytics) using FastAPI, Django/DRF, and optional frameworks such as BentoML.
- Implement LLM orchestration (LangChain, LangGraph, PydanticAI), prompt engineering, and retrieval‑augmented generation (RAG) pipelines.
- Develop voice‑to‑text, computer‑vision, and NLP capabilities for customer‑facing and internal tools.
- Deploy, monitor, and scale ML models via Docker/Kubernetes, MLflow/Kubeflow, or similar MLOps platforms on AWS, GCP, or Azure.
- Write unit, integration, and performance tests; maintain CI/CD pipelines and enforce code‑review standards.
- Coordinate with security, data privacy, and compliance teams to safeguard PII and mitigate prompt‑injection risks.
- Document APIs, data flows, model metadata, and operational runbooks.
Required Skills:
- 5+ years of professional Python development (FastAPI, Django, DRF).
- Strong experience deploying AI/ML models in production, including containerization and cloud orchestration.
- Hands‑on with PyTorch, TensorFlow, HuggingFace, and LLM orchestration libraries.
- Expertise in context‑engineering, prompt tuning, and RAG vector‑store integration.
- Familiarity with asynchronous streaming APIs and real‑time microservice design.
- Proficiency with AI‑assisted coding tools (GitHub Copilot, Claude Code, etc.).
- Solid knowledge of CI/CD, Git, testing frameworks, and software engineering best practices.
- Excellent problem‑solving, communication, and cross‑functional collaboration skills.
Required Education & Certifications:
- Bachelor’s degree in Computer Science, Software Engineering, or related field (Master’s preferred).
- Relevant certifications (e.g., AWS Certified Machine Learning, GCP Professional Data Engineer, Azure AI Engineer Associate) are an advantage but not mandatory.