Python AI Engineer

Python AI Engineer (Prompt & Agentic Systems)

Applying for this role is straight forward Scroll down and click on Apply to be considered for this position. Location: Hybrid –Atlanta, GA (3 days a week onsite) Client: Retail client About the Role 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 productionize intelligent features—ranging from retrieval-augmented generation (RAG) to autonomous tasking agents integrated with internal tools and APIs. Key Responsibilities 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. Required Qualifications 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). Nice to Have 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/Next.js) or chat UI patterns. xsgimln Domain knowledge in HR/ATS, customer support, or internal enterprise workflows.