AI Engineer Python, Agentic Systems

Python AI Engineer (Prompt & Agentic Systems)

Location: Hybrid Atlanta GA (3 days/week onsite)
Client: Retail client

About the Role

Were 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). Youll design develop and productionize 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. LangGraph/LangChain/AutoGen or in-house equivalents).
• Vector databases (e.g. FAISS Chroma Pinecone Weaviate) and search relevance tuning.
• Familiar with MLOps/DevOps: Docker CI/CD monitoring (Prometheus/Grafana) logging (OpenTelemetry) 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 LlamaIndex/LangChain.
• Familiarity with streaming UIs and structured outputs (JSON Pydantic schemas).
• Background in LLM finetuning RLHF/DPO or synthetic data generation.
• Front-end basics for AI UX (React/) or chat UI patterns.
• Domain knowledge in HR/ATS customer support or internal enterprise workflows.