AI Engineer

About Us

Automation Anywhere is the leader in Agentic Process Automation (APA), transforming how work gets done with AI-powered automation. Its APA system, built on the industry’s first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analytics—all delivered with enterprise-grade security and governance. Guided by its vision to fuel the future of work, Automation Anywhere helps organizations worldwide boost productivity, accelerate growth, and unleash human potential.

QUALIFICATIONS

Education: Bachelor’s or Master’s in CS, AI/ML, Data Science or equivalent practical experience

Experience: 4–7 years in software engineering; 3+ years building production-grade automation solutions

Demonstrated end-to-end delivery of agentic AI systems or complex enterprise RPA prototypes

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