AI Engineer
Actively Reviewing the ApplicationsAutomation Anywhere
India, Vadodara, Gujarat
Full-Time
On-site
Posted 3 weeks ago
•
Apply by June 15, 2026
Job Description
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.
Job Description
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.
Job Description
- We are looking for a full-stack associate to design, configure, and deploy AI Agents and intelligent automation solutions on an enterprise-grade Automation Anywhere GenAI platform while leveraging RAG (Retrieval-Augmented Generation) as the knowledge foundation for the Automation Anywhere Enterprise Knowledge Base (EKB).
- Full-stack knowledge of candidate will be used to integrate upstream full stack bots/APIs and downstream AAA Enterprise RPA Gen1, Gen2 (DocAI), and Gen3 (GenAI) bots, and vice versa.
- The Associate will define RAG success criteria (retrieval accuracy, hallucination thresholds) and KPIs; inventory knowledge sources (documents, databases) as an Enterprise Knowledge Base.
- Data sourcing plan and design end-to-end Agent flows covering triggers, KB retrieval, LLM calls, and actions.
- KB structure (chunking, fine-tuning) will be defined and built using EKB. Prototype prompts and Agents will be developed to produce functional test Chat/Agent prototypes.
- Agents and Prompts will be configured with a selected LLM (Gemini/GPT-4) to generate Agent configurations and prompt templates. Downstream API tasks (RPA steps/external calls) will be integrated, followed by UAT deployment (publish Agent/KB).
- Upon successful UAT, the solution will be published to Production via the Production Control Room, with BOT/Agent monitoring as part of Hypercare.
- Education: Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or equivalent practical experience.
- Experience: 1-3 years in software engineering; 1+ years building AI-powered automation solutions in production.
- Certifications (Preferred): Cloud AI certifications (AWS, Azure, GCP); RPA certifications such as UiPath or Automation Anywhere.
- Scope & Growth Path: Implement designs under Senior Engineer guidance; progress toward independent stakeholder ownership.
- Hands-on experience with LangChain, LangGraph, AutoGen, or CrewAI.
- Core agent patterns: tool usage, memory, multi-step reasoning, validation and guardrails.
- LLM APIs: OpenAI, Anthropic, Gemini, or open-source models; structured prompt engineering.
- End-to-end RAG pipelines: ingestion, chunking, embedding, vector stores, and retrieval evaluation.
- Hands-on with vector databases; ability to diagnose and improve retrieval quality.
- Hands-on with UiPath, Automation Anywhere, or Microsoft Power Automate.
- Bot workflows with exception handling, logging, and enterprise system integrations.
- Strong Python skills; experience with cloud AI services, APIs, data pipelines, and event-driven systems.
- Exposure to LoRA / QLoRA fine-tuning approaches.
- Agent observability and evaluation tooling such as LangSmith or Arize.
- Build and maintain multi-agent workflows from solution design through production deployment.
- Implement root-cause analysis, ERP/CRM/ITSM integrations, and robust error handling.
- Design and operate end-to-end RAG systems; continuously monitor and improve retrieval quality.
- Develop and maintain RPA tasks; integrate AI agents with RPA and business logic.
- Write unit and integration tests; contribute to CI/CD pipelines and deployment automation.
- Collaborate with senior engineers; document pipelines, agent configurations, and operational runbooks.
Required Skills
Engineering
Automation
Monitoring
Python
Sourcing
AWS
CI/CD Pipelines
RPA
UiPath
Automation Anywhere
Azure
LangChain
KPIs
Agent Monitoring
Data Science
CI/CD
Anthropic
ERP
CRM
RAG
Validation
Intelligent automation
GPT
Software engineering
ITSM
Solution design
Vector
Guardrails
Ingestion
Business logic
Integration tests
Data pipelines
Event-driven
UAT
Logging
Embedding
Fine-tuning
OpenAI
Reasoning
Microsoft Power
Chunking
Microsoft Power Automate
Versa
LLM APIs
Retrieval-Augmented Generation
GenAI
Prompt engineering
Gemini
Retrieval
Error handling
Data sourcing
RAG systems
Memory
Observability
Exception Handling
AI/ML
Computer Science
LLM
LoRA
AAA
Augmented Generation
Prototype
Retrieval-augmented
Vector Databases
Langgraph
AutoGen
AI Agents
Exception
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