Agentic AI Automation Engineer

HRS Group; Sahibzada Ajit Singh Nagar; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

HRS Group in Mohali, India is seeking a Mid-Level AI Agentic Engineer to join the AI-Workflow programme and build production AI crews that support Finance, Controlling, Operations, Customer Service, and HR. This is not a research role, you will ship live autonomous crews with measurable outcomes from day one.

You will work with DSPy, N8N, LangGraph, and AWS Bedrock, ensure 100% unit tests, implement guardrails, and contribute to a shared platform while collaborating with cross‑functional teams.

Qualifications

• 3-5 years of AI/ML development experience, with 1+ years in agentic AI or production deployments.
• Hands-on experience with DSPy signatures and assertion guards.
• Exposure to at least one agent framework such as LangGraph, Google ADK, Bedrock AgentCore, LangChain, or CrewAI.
• Experience building N8N workflows in production codebases.
• Ability to design testable escalation boundaries in live systems.
• GitHub projects or shipped systems demonstrating work.

Responsibilities

• Build production AI crews from structured process descriptions with assertion guards and orchestration patterns.
• Integrate N8N workflow automation and connectors to operational systems.
• Collaborate with Automation Agent to scaffold DSPy modules and improve generated output.
• Define escalation boundaries and guardrails before shadow deployment.
• Deliver crews with comprehensive runbooks, unit tests, and New Relic instrumentation.
• Contribute reusable patterns to the crew library and peer reviews.

Skills

AI/ML development

DSPy typing

Agent frameworks

N8N workflow

Escalation design

Code samples

Tools

LangGraph

Google ADK

Amazon Bedrock AgentCore

LangChain

CrewAI

Job description

POSITION

We are seeking a Mid-Level AI Agentic Engineer to join the AI-Workflow programme and build the autonomous crew systems that augment HRS operations across Finance, Controlling, Operations, Customer Service, Customer Experience, and HR. This is not a research role or a prototype environment — you will be building production AI crews that handle live operational workflows for real departments, with real outcomes measured from day one.

Mid‑Level AI Engineer — Agentic AI

AI-Workflow Programme | Mohali, On-Site

We are seeking a Mid‑Level AI Agentic Engineer to join the AI-Workflow programme and build the autonomous crew systems that augment HRS operations across Finance, Controlling, Operations, Customer Service, Customer Experience, and HR. This is not a research role or a prototype environment — you will be building production AI crews that handle live operational workflows for real departments, with real outcomes measured from day one.

CHALLENGE

Crew Development & Implementation

• Build operational AI crews from structured To‑Be process descriptions using DSPy typed signatures with assertion guards and agent workflow orchestration patterns such as state machines, human‑in‑the‑loop checkpoints, and resumable execution.
• Implement N8N workflow automation and JSON integration connectors linking crews to operational systems including Zammad, Genesys, and enterprise back‑office platforms.
• Work directly with the Automation Agent to scaffold DSPy modules and agent workflows — extending and improving generated output, not accepting it verbatim.
• Design and encode specific, testable escalation boundaries for every crew before shadow deployment — grounded in real process context, not generic confidence thresholds.
• Deliver every crew with 100% unit test coverage, a complete runbook, and New Relic instrumentation live before go‑live — these are deployment gates, not aspirational standards.
• Contribute reusable patterns to the shared crew library and peer‑review modules built by other engineers on the team.

Technical Execution & Quality

• Implement agent memory management using explicit typed state, structured context handling, and clear handoff boundaries to prevent context degradation across multi‑step operational workflows.
• Apply three‑layer output validation — DSPy assertions, output validators, and policy enforcer — on every crew module before merge.
• Build and validate test suites covering non‑deterministic edge cases and failure modes — not just happy paths — using the QA Agent's generated baseline as a starting point.
• Integrate crews with AWS Bedrock model routing (Claude Haiku/Sonnet) and work within the EKS and Terraform IaC stack managed by DevOps.
• Maintain guardrails configuration for every crew — escalation triggers, human approval gates, and policy enforcement — encoded in config before any crew enters shadow deployment.
• Participate in weekly DSPy evaluation cycles against gold‑standard baselines to validate crew output quality and flag drift.

Observability & Production Operations

• Instrument every crew with New Relic metrics from day one: throughput, error rate, latency, escalation rate, and cost per task — observability is a deployment prerequisite, not an afterthought.
• Actively diagnose and resolve production failure modes: memory drift across multi‑step workflows, hallucination under low‑confidence RAG retrieval, context degradation in long‑running state machines, and prompt injection via untrusted integration inputs.
• Use post‑deployment observability data to identify improvement candidates and raise them in RAID — closing the feedback loop the Observability Agent depends on.
• Contribute to the continuous improvement cycle: every crew in production is a measurement and improvement loop, not a delivery milestone.

Collaboration & Build Platform

• Work within the 10‑day delivery lifecycle — Request -> Discovery -> Design -> Development -> QA -> CI/CD -> Monitoring -> Continuous Improvement — delivering to standard at each stage.
• Collaborate with the PM during Discovery to assess process automation feasibility using FUDV scoring — frequency, uniformity, digitisation, volume — and push back credibly where AI reliability or data quality is not there yet.
• Contribute to Thursday technical reviews and Friday retrospectives with substantive input — not status updates but engineering judgment.
• Use the build agent platform as a personal productivity multiplier — flag platform gaps via RAID rather than working around them silently.

FOR THIS EXCITING MISSION YOU ARE EQUIPPED WITH…

Agentic AI Technical Skills

• 3‑5 years of experience in AI/ML development with 1+ years in agentic AI or advanced LLM applications shipped to a production environment — not prototype or hackathon experience.
• Hands‑on experience with DSPy typed signatures and assertion guards — not just LangChain familiarity.
• Practical exposure to at least one agent framework or platform such as LangGraph, Google ADK, Amazon Bedrock AgentCore, LangChain, CrewAI, or equivalent; the role values transferable agentic engineering patterns over any single required framework.
• Experience building and committing N8N workflow automation in a production codebase.
• Demonstrated ability to design specific, testable escalation boundaries in a live operational AI system.
• Can show their work — a GitHub profile, a shipped system, or a concrete before/after on a workflow they automated carries more weight than academic credentials.

AI Engineering Capabilities

• Strong Python programming skills with AI/ML libraries and practical agentic engineering patterns; able to work across frameworks when needed, with exposure to at least one of LangGraph, Google ADK, Amazon Bedrock AgentCore, LangChain, CrewAI, or equivalent.
• Production experience with AWS Bedrock or equivalent cloud‑based LLM routing and model management.
• Knowledge of vector databases, embedding systems, and retrieval‑augmented generation — including retrieval quality assessment and hallucination mitigation.
• Understanding of MLOps and AIOps practices: CI/CD for AI systems, evaluation harnesses, and gold‑standard baseline testing.
• Hands‑on experience with New Relic or equivalent observability tooling for production AI systems — metric design, dashboard instrumentation, and anomaly diagnosis.
• Familiarity with containerisation, EKS, and Terraform IaC sufficient to work within a DevOps‑managed infrastructure without creating integration delays.

Development & Process Skills

• Test‑driven development for non‑deterministic systems — 100% unit test coverage before merge is a non‑negotiable standard in this team.
• Experience with agile delivery in a time‑boxed sprint model — able to take a structured process description from design to shadow deployment within a 10‑day lifecycle.
• Strong code documentation discipline — every module peer‑handoff ready, every runbook complete during build, every decision traceable in RAID.
• Ability to work within an architecture set by a Tech Lead — executing with full ownership and quality pride within defined guardrails, escalating cleanly when constraints need revisiting.

Professional Skills

• Writes to be understood, not to be impressive — RAID entries a director can triage, runbooks a department SME can follow, code a peer can extend without asking the author.
• Calm under non‑determinism — diagnoses production failures methodically using observability data rather than thrashing or going silent.
• Dog‑food mentality — uses the build agent platform to accelerate their own work and actively contributes to improving it.
• Mission‑driven — understands that the goal is workforce augmentation, not automation for its own sake, and builds every crew with that principle at its centre.

Preferred Experience

• Domain experience in at least one of: Finance, Controlling, HR, Operations, Customer Service, or Customer Experience — brings escalation boundary instinct that no intake card can fully replicate.
• Experience with enterprise system integrations: Zammad, Genesys, UiPath, or equivalent CRM/CX/RPA platforms.
• Familiarity with ChromaDB/Milvus/pgVector or equivalent vector store for RAG pipeline development.
• Experience contributing to a shared pattern library or internal engineering knowledge base in a multi‑engineer AI team.