AI Engineer

Certifications (Preferred): AWS, Azure or GCP; RPA platforms such as UiPath or Automation Anywhere

Scope: Leads solution architecture independently, owns LLM adaptation strategy end-to-end, mentors junior engineers; leads stakeholder discovery workshops

SKILLS

Agentic AI: LangChain/LangGraph, AutoGen, CrewAI

Agent patterns: tool use, memory, multi-agent coordination, guardrails, failure recovery

LLM Fine-Tuning & Adaptation: LoRA/QLoRA with HuggingFace PEFT or Unsloth; Dataset prep, evaluation benchmarking, model versioning; Serving fine-tuned models:vLLM,GPTQ, GGUF

RAG & Vector Infrastructure: Pinecone, Weaviate, Qdrant; embeddings, retrieval evaluation

RPA: UiPath, Automation Anywhere, Power Automate in production

Engineering: Python (production quality); Cloud AI services (Bedrock,Azure, OpenAI, Vertex AI)

RESPONSIBILITIES

Agent Design & Engineering:

Architect multi-agent systems with branching logic, exception handling & human-in-the-loop escalation.

Define agent tool integrations, memory, context management & state persistence.

LLM Adaptation Strategy:

Own fine-tuning strategy (fine-tune vs RAG vs prompt engineering) and deliver end-to-end

Manage GPU training runs, model merging, quantization & production serving

RPA & HYBRID AUTOMATION:

Build RPA task bots as execution layers within agentic workflows

Architect AI agent ↔ RPA handoff logic and exception management

Production & Operations:

Build agent evaluation frameworks; implement observability & tracing (LangSmith, Arize)

CI/CD for agent/model deployments; diagnose hallucination, tool misuse & cost runaway

Stakeholder & Leadership:

Lead use-case discovery workshops; communicate architecture trade-offs to non-technical audiences

All unsolicited resumes submitted to any @automationanywhere.com email address, whether submitted by an individual or by an agency, will not be eligible for an agency fee.