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Enterprise software delivery since 2009Scrum leadership since 2011Governed AI delivery since 2025

Santhosh Reddy

Senior Scrum Master & Agile Coach for AI-Enabled Engineering Teams

15+ years helping enterprise software teams deliver predictable outcomes through Agile coaching, quality engineering, technical leadership, and AI-assisted engineering workflows since 2025.

Not a ceremony facilitator: a delivery lead who recovers late programs, turns quality around with evidence, and puts governance around AI before it touches the release.

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enterprise Agile delivery
15+ years
platforms delivered in parallel at TELUS
5
releases coordinated each year
~29
QA organization coordinated
18-member
features recovered in ~6 sprints at Boeing
12
quarterly incidents at QHR, down from hundreds
Single digits

Enterprise results

Four programs, four different problems, measured outcomes.

Recovery, quality transformation, cross-platform coordination, and delivery predictability. Each story below states the constraint, the intervention, the trade-off taken, and the result.

TELUS

Telecommunications · Video Streaming
2021–Present

Capability demonstratedEnterprise coordination: running delivery, quality visibility, and AI governance across five platforms without fragmenting ownership.

Scaling Delivery and Quality Visibility Across a 5-Platform Streaming Ecosystem

Situation

A large-scale video streaming ecosystem shipping across Android Mobile, iOS/tvOS, Web, Smart TV, and Android TV, with 5 client development teams, an 18-member QA organization, and approximately 29 releases a year.

Constraint

QA operated in silos, development and QA tracked work in duplicate, and testing ran behind development, so no one had a single trustworthy picture of release readiness across platforms.

Intervention

  • Established executive-level Jira and Confluence dashboards covering release readiness, defect trends, and automation health
  • Led cross-functional workshops aligning Product, Development, QA, and Scrum Masters on same-sprint testing and integrated delivery
  • Partnered with QA Automation teams across E2E, regression, smoke, and sanity suites
  • Introduced AI-assisted workflows for defect documentation, story creation, and release communications, governed by human review and traceability guardrails, beginning in 2025

The trade-off

AI adoption was gated behind governance rather than rushed: human review and traceability came first, so quality and delivery standards held while manual reporting effort went down.

platforms under one delivery view
5
annual releases coordinated
~29
QA organization aligned on same-sprint testing
18-member
governed AI-assisted delivery introduced
2025

QHR Technologies

Healthcare · Accuro EMR
2020–2022

Capability demonstratedQuality transformation: connecting Agile practice to a measured, sustained drop in production incidents.

From Hundreds of Incidents Per Quarter to Single-Digit Levels in ~8 Sprint Cycles

Situation

First Scrum Master hired at QHR Technologies, supporting Accuro EMR, an electronic medical record platform used by family practitioners and hospitals, starting with 2 development teams.

Constraint

Production incidents were arriving by the hundreds each quarter, there were no Definition of Ready or Definition of Done standards, and 5–10 unplanned requests per sprint kept breaking delivery focus.

Intervention

  • Introduced Scrum ceremonies, Definition of Ready, Definition of Done, and structured backlog refinement across the initial teams
  • Built intake governance so unplanned work was triaged instead of silently absorbed
  • Partnered with Engineering and QA leadership to move testing into the same sprint as development
  • Tracked production incident trends and Agile maturity so improvement was measured, not assumed

The trade-off

Coaching expanded from 2 teams to the wider organization only after incident-trend data showed the practices working: adoption followed evidence rather than mandate.

quarterly production incidents
Hundreds to single digits
sprint cycles to turn quality around
~8
teams coached as results proved out
2 to org-wide

AT&T

Telecommunications · eCommerce
2018–2020

Capability demonstratedPredictability under pressure: intake governance that protects commitments without walling off the team.

Containing Sprint Disruption and Restoring Delivery Predictability on an eCommerce Team

Situation

An 8-member eCommerce development team was absorbing 5–10 unplanned requests every sprint. Commitments kept slipping, burndown data showed it, and testing coverage lagged behind development.

Constraint

Every incoming request was being accepted without triage, architecture was often unsettled at planning time, and the team had a single automation resource.

Intervention

  • Created a separate intake board with structured triage for out-of-scope requests
  • Made trade-offs explicit in standups: planned work and ad-hoc requests reviewed side by side, with deliberate decisions on what displaced what
  • Enforced Definition of Ready so architecture and design questions were settled before sprint planning
  • Strengthened code review to a two-reviewer standard and helped QA build automation within the same sprint

The trade-off

Unplanned work was made visible and negotiated instead of refused or silently absorbed, protecting sprint commitments while staying responsive to the business.

unplanned requests per sprint brought under governance
5–10
architecture settled before planning, not during the sprint
Ready
code review standard with same-sprint automation
Two-reviewer

Boeing

Aviation · Electronic Flight Bag
2017–2018

Capability demonstratedProgram recovery: converting a delayed platform stream into a sequenced, resourced, and delivered roadmap.

Delivering 12 Customer-Critical Features Across ~6 Sprint Cycles Without Adding Headcount

Situation

Boeing Aviator, an Electronic Flight Bag platform used by pilots and first officers, shipped on iOS and Windows. The Windows stream had fallen approximately 6 months behind iOS, and airline clients were waiting on critical features.

Constraint

No additional headcount was available, and the work spanned one onshore and one offshore team that had to move as a single delivery unit.

Intervention

  • Drafted a phased recovery roadmap prioritizing 12 features by observed client usage on iOS: high first, then medium, then low
  • Engaged project and senior program managers to borrow Architecture, UX, BA, and QA capacity from the iOS team instead of hiring
  • Ran daily coordination bridging the onshore and offshore teams to keep sprint delivery aligned

The trade-off

Recovery was sequenced by client usage rather than backlog age, and capacity was borrowed rather than added, trading roadmap breadth for speed on the features clients actually depended on.

customer-critical features delivered
12
sprint cycles from behind to delivered
~6
headcount added to the program
Zero
Boeing Certificate of Achievement
Award

Problems I take on

If this list sounds like your delivery organization, we should talk.

Situations I have been brought in to fix, each linked to where the evidence lives on this page.

  • A program is behind and nobody trusts the recovery plan

    Sequence recovery by what customers actually use, negotiate shared capacity instead of waiting for headcount, and report progress against a phased roadmap leadership can verify.

    Boeing: 12 features in ~6 sprints

  • Quality problems surface in production, not in the sprint

    Move testing into the same sprint as development, set Definition of Ready and Done standards teams actually follow, and track incident trends so improvement is measured rather than claimed.

    QHR: hundreds of incidents to single digits

  • Cross-team releases have no shared readiness picture

    Build one delivery view across teams and platforms: release readiness, defect trends, and automation health in dashboards that engineering and executives read the same way.

    TELUS: 5 platforms, ~29 releases a year

  • Unplanned work keeps breaking sprint commitments

    Give unplanned work a front door: structured intake, explicit trade-off conversations, and visible cost, so the team stays responsive without silently losing its sprint.

    AT&T: 5–10 requests per sprint governed

  • Development and QA operate as separate worlds

    Align both on one plan: shared tracking instead of duplicate boards, same-sprint testing instead of handoffs, and automation health made visible to everyone who ships.

    TELUS: 18-member QA org, one delivery view

  • Work starts before anyone agrees what done means

    Enforce Definition of Ready with teeth: acceptance criteria challenged before planning, architecture questions settled up front, and stories that are honestly buildable or honestly not ready.

    AT&T and QHR: DoR and DoD discipline

  • Leadership finds out about delivery risk when the date slips

    Translate engineering reality into executive-level delivery signals: risk surfaced early, dependencies mapped, and readiness reported in terms leaders can act on.

    TELUS: executive delivery dashboards

  • Teams want AI in the workflow, but nothing governs it

    Adopt AI where it demonstrably reduces manual effort, and wrap it in guardrails: human review before anything ships, traceability on every output, and quality standards that do not move.

    TELUS: governed AI delivery since 2025

Operating model

From customer problem to production feedback.

The delivery system I run: outcome clarity up front, dependencies and risk made visible, quality gated in-sprint, AI assistance governed by human review. Open any stage to see it in practice.

  1. Customer Problem

    Understand the real user or business problem before jumping into implementation.

    Customer-first scoping at TELUS

    In practiceHide

    Customer-first scoping at TELUS

    At TELUS, delivery planning began from customer outcomes: acceptance criteria were written to reflect real user needs across five streaming platforms before implementation was scoped.

    TELUS — Professional Journey →

  2. Delivery Planning

    Convert the problem into scoped work, risks, acceptance criteria, and clear validation paths.

    Boeing Aviator recovery roadmap

    In practiceHide

    Boeing Aviator recovery roadmap

    Drafted a phased recovery roadmap with usage-prioritized sequencing for the Boeing Aviator program, delivering 12 customer-critical features in approximately 6 sprint cycles.

    Boeing — Enterprise Results →

  3. AI-Assisted Engineering

    Use AI tools to accelerate implementation while keeping architecture, review, and decision ownership human-led.

    TELUS AI-assisted delivery, since 2025

    In practiceHide

    TELUS AI-assisted delivery, since 2025

    Beginning in 2025 at TELUS, introduced AI-assisted workflows for reporting and backlog administration, with human-review guardrails and traceability across five product platforms.

    AI Delivery, Governed →

  4. Quality Gates

    Validate through automation, exploratory review, release checks, and production-monitoring feedback.

    TELUS QA coordination and release readiness

    In practiceHide

    TELUS QA coordination and release readiness

    Coordinated E2E, regression, smoke, and sanity automation across an 18-member QA organization at TELUS, driving same-sprint testing adoption and release readiness reporting.

    TELUS — Enterprise Results →

  5. Continuous Improvement

    Inspect outcomes, refine practices, and carry learning forward: closing the loop from delivery back to discovery.

    Structured intake improvement at AT&T

    In practiceHide

    Structured intake improvement at AT&T

    At AT&T, introduced structured intake practices that managed 5 to 10 unplanned requests per sprint, strengthening Definition of Ready discipline and reducing delivery disruption.

    AT&T — Enterprise Results →

AI delivery, governed

AI in the delivery workflow, with the guardrails stated up front.

Since 2025: AI-assisted delivery practices introduced at enterprise scale, and working AI systems built hands-on. In both cases the interesting part is the governance, not the tooling.

In the enterprise

At TELUS, introduced AI-assisted workflows for defect documentation, backlog authoring, sprint and release reporting, and stakeholder communications using Claude Code and generative AI, reducing manual administrative effort across a five-platform delivery organization. Adoption came with governance: delivery guardrails defined before rollout, not after.

Human review
AI-assisted output is reviewed and approved by a person before it is published or actioned.
Traceability
Every assisted artifact stays traceable to its source and its reviewer. Nothing ships unattributed.
Unchanged quality bar
Delivery and quality standards hold. AI reduces manual effort; it does not lower the gate.

Built hands-on

Personal engineering projects, started in 2026, that apply the same delivery discipline to AI systems: validation gates, bounded failure handling, and operational visibility, in working code.

Personal project · 2026

Animation Engine

A deterministic 2D character animation engine producing YouTube Shorts from JSON scene definitions. An AI story compiler turns narrative prompts into fully animated scenes via Claude and OpenAI APIs using forced tool-use and a Gate A deterministic retry loop. Phase 11 engine: 1,078+ passing tests, multi-character SVG rendering (760×400px → MP4 via ffmpeg), and a live end-to-end AI compile completed in 47 seconds.

What made it hard

LLM output is nondeterministic; rendering must not be. Every AI-generated scene had to arrive as valid, schema-conforming structure — never free text — and produce frame-perfect, reproducible output across multi-character scenes.

Governance decisions
  • Forced tool-use: the model must emit a structured scene definition against a schema — free-text output is never parsed
  • Gate A validation: every AI output is structurally validated before it can touch the renderer, with a bounded, deterministic retry loop on failure
  • Provider-agnostic adapters: the same compile interface runs Claude or OpenAI, so no single vendor owns the pipeline
  • 1,078+ regression tests guard every AI-facing change before merge
passing tests guarding the pipeline
1,078+
pull requests merged under review discipline
74+
live end-to-end AI compile
47 s

Why it matters hereThe same discipline applied to enterprise delivery — validation gates, bounded failure handling, and traceability — implemented in working code rather than recommended in a slide deck.

JavaScriptPythonSVGFastAPIffmpegClaude APIOpenAI APIAnimation

Personal project · 2026

AI Control Center

A local-only Python monitoring dashboard for an AI workstation: CPU, RAM, disk, NVIDIA GPU utilization, Ollama model status, and registered AI agent health — all in a single auto-refreshing page. Read-only; loopback-bound; no external data exposure. Includes optional LAN access mode with password protection and brute-force lockout.

What made it hard

Aggregating heterogeneous health signals — process state, GPU telemetry, Ollama REST APIs, filesystem checks — into one stable, auto-refreshing view that stays trustworthy enough to act on.

Governance decisions
  • Read-only by design: the dashboard observes, it cannot mutate the systems it monitors
  • Loopback-bound by default, with LAN access opt-in behind password protection, session timeout, and brute-force lockout
  • Path-traversal protection on every file-serving endpoint
  • API contract tests and browser-level Playwright tests on monitoring endpoints

Why it matters hereOperational visibility thinking — the single-pane-of-glass instinct behind enterprise delivery dashboards, applied hands-on to AI infrastructure.

PythonFastAPIpsutilJinja2OllamaNVIDIA

This site is part of the evidence: it is built with AI-assisted engineering under the same rules — typed publication schemas gate what can ship, every metric carries a source reference, and human review precedes every merge.

Professional journey

Fifteen-plus years of widening delivery scope.

From business analysis to Scrum leadership, Agile coaching, enterprise cross-team delivery, and since 2025, AI-enabled delivery governance.

I help engineering teams deliver software more predictably by combining Agile coaching, quality engineering, technical leadership, and practical AI adoption. My experience spans enterprise software delivery across telecommunications, aviation, healthcare, financial services, and eCommerce, with a focus on improving delivery confidence, engineering quality, and continuous improvement. Beginning in 2025, I expanded this work into AI-assisted engineering and delivery workflows, using Claude Code and GPT review patterns to improve implementation discipline, documentation, traceability, and maintainability.

  1. Business analysis
  2. Scrum Master leadership
  3. Agile coaching
  4. Enterprise delivery & quality
  5. Governed AI delivery (2025)

  1. Senior Scrum Master / Agile Delivery Lead

    Enterprise delivery · governed AI since 2025

    TELUS · 2021 – Present

    Agile delivery lead for a large-scale video streaming ecosystem spanning 5 product platforms, 5 development teams, and an 18-member QA organization. Coordinated cross-platform delivery, release readiness, quality visibility, and stakeholder alignment. Beginning in 2025, introduced AI-assisted delivery workflows using Claude Code and generative AI for defect documentation, backlog management, and stakeholder reporting, supported by human-review and traceability guardrails.

  2. Scrum Master / Agile Coach

    Quality transformation

    QHR Technologies · 2020 – 2022

    First Scrum Master hired at QHR Technologies, initially coaching 2 development teams and later expanding Agile coaching across the organization. Introduced structured Agile and quality practices that helped reduce quarterly production incidents from hundreds to single-digit levels in approximately 8 sprint cycles.

  3. Scrum Master / Agile Coach

    Agile coaching

    AT&T · 2018 – 2020

    Scrum Master and Agile Coach for an 8-member eCommerce development team at AT&T. Introduced structured intake and planning practices to manage 5–10 unplanned requests per sprint, reducing delivery disruption while strengthening Definition of Ready discipline and collaboration across development, QA, and product stakeholders.

  4. Scrum Master

    Delivery recovery

    Boeing · 2017 – 2018

    Scrum Master for the Boeing Aviator Electronic Flight Bag platform (iOS and Windows). Led recovery coordination for a Windows product stream approximately 6 months behind schedule, delivering 12 customer-critical features across approximately 6 sprint cycles without increasing project headcount. Received a Boeing Certificate of Achievement for leadership and stakeholder alignment.

  5. Scrum Master

    Agile transformation

    Sammons Financial Group · 2016 – 2017

    Supported an Agile transformation across multiple software development teams moving from Waterfall to Agile delivery, improving stakeholder transparency, release planning, backlog management, and team performance visibility.

  6. M.S. Information Technology, full-time study

    Graduate study

    Missouri Western State University · 2014 – 2016

    Full-time graduate study in Information Technology, completed 2016.

  7. Scrum Master

    Scrum Master leadership

    Avaya · 2011 – 2014

    Facilitated Scrum adoption across multiple teams in a communications technology organization, with a focus on dependency management, release coordination, stakeholder communication, and Agile coaching.

  8. Business Analyst

    Business & delivery foundation

    Flipkart · 2009 – 2011

    Requirements gathering, stakeholder workshops, process and gap analysis, and UAT coordination for eCommerce initiatives.

Where I fit

Roles this experience is built for.

Each with the evidence a hiring manager would ask for first.

Primary role fit

  • Senior Scrum Master

    15+ years across enterprise programs at TELUS, Boeing, AT&T, and QHR. PSM II certified.

  • Agile Delivery Lead

    Coordinates 5 platforms, 5 client teams, and ~29 annual releases in a streaming ecosystem at TELUS.

  • Agile Coach

    First Scrum Master hired at QHR; coaching grew from 2 teams to organization-wide on measured results.

  • Quality Transformation Lead

    Drove quarterly production incidents from hundreds to single digits in ~8 sprint cycles at QHR.

Additional leadership strengths

  • Technical Program Coordination

    Coordinated multi-team program recovery at Boeing: 12 customer-critical features in ~6 sprints with no added headcount.

  • Governed AI-Assisted Delivery

    Established AI governance and delivery guardrails at TELUS since 2025, and builds governed AI systems hands-on.

Core disciplines

  • Agile Coaching
  • Scrum Leadership
  • AI-Assisted Delivery
  • Quality Engineering
  • Delivery Excellence
  • Engineering Governance
  • Technical Program Delivery
  • Continuous Improvement

Credentials

Education and certifications.

Education

Master of Science (M.S.), Information Technology

Missouri Western State University, USA — 2016

Certification

Professional Scrum Master I (PSM I)

Scrum.org — 2017 · Lifetime

Certification

Professional Scrum Master II (PSM II)

Scrum.org — 2025 · Lifetime

Contact

Start the conversation.

Open to conversations about delivery leadership, quality engineering, and AI-enabled team workflows.

Email SanthoshConnect on LinkedIn
Email
santhoshkasula1987@gmail.com
Location
Calgary, Alberta, Canada
Résumé
Available on request