Mid-Level AI Engineer — Agentic AI

AI-Workflow Programme | Mohali, On-Site

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.

You will work within a "crews building crews" model: a platform of seven build agents (PM,

Architect, Automation, QA, SRE, Documentation, Observability) scaffolds, tests, and

documents the operational crews you build. Your job is to close the gap between agent

scaffolded output and production-ready code — working directly with the Tech Lead, the

PM, and the build agent platform to deliver tested, instrumented, and documented crews

within a 10-day delivery lifecycle.

The mission is workforce augmentation. AI handles the volume. Humans handle the

judgement. Every crew you build encodes that principle in every escalation boundary,

every guardrail, and every human-in-the-loop gate.

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

Agentic AI Automation Engineer