Python AI Engineer; Prompt & Agentic Systems

Job Title:
Python AI Engineer (Prompt & Agentic Systems)

Location:

Hybrid –Atlanta, GA (3 days/week onsite)

We’re looking for a hands‑on engineer who can build AI‑enabled applications end‑to‑end using Python
, with strong skills in prompt engineering and agentic system design (multi‑agent/orchestrated AI workflows). You’ll design, develop, and product ionize intelligent features—ranging from retrieval‑augmented generation (RAG) to autonomous tasking agents integrated with internal tools and APIs.

• Design & Build AI Services: Develop Python-based back‑end services that integrate LLMs for reasoning, extraction, summarization, and decision support.
• Prompt Engineering: Craft, version, and evaluate prompts/system instructions; design guardrails, test prompt variants, and optimize for reliability, latency, and cost.
• Agentic Systems: Architect and implement autonomous/multi‑agent workflows
  —planning, tool‑use, memory, error recovery, and human‑in‑the‑loop controls.
• RAG Pipelines: Implement document ingestion, chunking, embeddings, vector search (semantic/re‑ranking), and grounding strategies.
• Evaluation & Observability: Define metrics and build eval suites for quality (accuracy, factuality, safety), and establish tracing/telemetry for LLM calls.
• API & Tool Integrations: Enable agents to use tools (internal APIs, search, databases, workflow engines); handle auth, rate limits, and fallbacks.
• MLOps / AIOps: Package, containerize, and deploy services (Docker/K8s); manage keys, secrets, CI/CD; support canary rollouts and cost governance.
• Security & Compliance: Apply data privacy principles, PII handling, redaction, prompt injection defenses, and audit logging.
• Cross‑Functional
  
  Collaboration:
  
  Partner with product, data, and security teams to translate requirements into reliable AI features.

• Strong Python (typing, async, testing, packaging) and experience building production APIs/services (FastAPI/Flask).
• Hands‑on with LLMs (OpenAI, Azure OpenAI, Anthropic, etc.) and embedding/RAG workflows.
• Proven prompt engineering experience (few‑shot strategies, tool‑use instructions, output schemas, function/tool calling).
• Experience with agent frameworks or custom agent orchestration (e.g., Lang Graph/Lang Chain/Auto Gen, or in‑house equivalents).
• Vector databases (e.g., FAISS, Chroma, Pinecone, Weaviate) and search relevance tuning.
• Familiar with MLOps/Dev Ops
  :
  Docker, CI/CD, monitoring (Prometheus/Grafana), logging (Open Telemetry), secrets management.
• Testing & Evals
  : unit/integration tests, offline evals, golden datasets, regression checks.
• Practical understanding of AI safety/guardrails (prompt injection, data leakage, jailbreak prevention).

• Experience with Azure (or AWS/GCP) AI services, key vaults, and networking.
• Knowledge of Model Context Protocol (MCP) or tool‑server patterns for secure tool access.
• Experience with retrievers (BM25, hybrid search),
  re‑rankers
  , or Llama Index/Lang Chain
  .
• Familiarity with streaming UIs and structured outputs (JSON, Pydantic schemas).
• Background in LLM fine tuning
  , RLHF/DPO
  , or synthetic data generation.
• Front‑end basics for AI UX (React/Next.js) or chat UI patterns.
• Domain knowledge in HR/ATS, customer support, or internal enterprise workflows.