Expert Manager, Technical Delivery

About the Role

Bain & Company is expanding its Technology Delivery capability across Asia Pacific (APAC) to support complex, large-scale technology transformations for leading global organizations.

We are seeking experienced Technical Program Managers and Technology Delivery leaders to lead cross-functional technical programs end-to-end, orchestrate multi-team SDLC and delivery models, and ensure system-level architectural integrity and engineering coherence.

This role sits at the intersection of strategy and engineering. You will partner closely with product, architecture, and engineering teams to translate enterprise ambition into durable business outcomes.

This role is open across our Southeast Asia and India offices.

What You’ll Do

1) Lead cross-functional technical programs end-to-end

• Lead complex programs frominceptionthrough production and sustained operations. Drive execution strategy, sequencing, and measurable outcomes across multiple engineering teams and stakeholders.
• Serve as thesingle-threaded owner for system-leveldeliveryhealth. Surface constraints early, make trade-offs explicit, and resolve risks that affect scope, quality, or launch readiness.
• Define and operationalizeexecution mechanismssuch as planning cadences, dependency tracking, release models, and risk thresholds to enable aligned decision-making at scale.
• Owncross-team risk, dependency, andcritical-pathmanagement. Actively unblock teams, resolve integration friction, and prevent silent failure modes in multi-system environments.
• Operatemetrics-first. Establish leading and lagging indicators fordeliveryhealth, including throughput, quality, NFR adherence, and release stability. Use data to improve engineering velocity and long-term system resilience.

2) Orchestrate multi-team SDLC and delivery models

• Design and evolvefit-for-purpose governancethat supports fast, aligned decisions across sponsor, program, and engineering levels while preserving system-level coherence.
• Structure and scalemulti-team delivery modelssuch as release trains, platform-stream alignment, and dependency governance based on architectural complexity and client maturity.
• Orchestratemulti-team SDLC in complex enterprise environments, including parallel agile and legacy cadences. Ensureintegrationsequencing, release predictability, and execution discipline.
• Establish and enforcedelivery guardrailsacrossrequirementsquality, technical design readiness, CI/CD rigor, testing strategy, and production readiness.
• Ownreleaseorchestration and go/no-go accountability. Ensure readiness across code, infrastructure, observability, rollback, operational support, and executive risk alignment.
• Drivetesting and validation rigor, both functional and non-functional. Embed measurable quality gates and prevent late-stage integration failures.

3) Ensure system-level architectural integrity and engineering coherence

• Apply systems thinking tomaintainarchitectural integrityacross services, platforms, and domains as programs scale.
• Makesystem-level trade-offsexplicit across business, product, and engineering constraints, such as time-to-market versus resilience or autonomy versus reuse.
• Institutionalizearchitecture and product decision qualityusing lightweight, traceable mechanisms such as ADRs and PDRs.
• Partner closely with architecture and engineering leadsto ensure architectural intent is deliverable, scalable, and operationally sustainable.
• Owncross-domain dependency mapping andintegrationreadiness.Identifysystemic risks across legacy systems, shared services, vendor components, and platform layers.
• OperationalizeNFRs, including performance, availability, resilience, security, and observability as measurable system commitments. Ensure they are embedded into backlogs, CI/CD pipelines, release criteria, and production monitoring.
• Protectlong-term system healthby managing technical debt visibility, capacity erosion risks, and architectural drift.

4) Embed modern AI/GenAI practices into delivery

• IntegrateAI-enabled delivery workflowsacross the SDLC, including analysis, documentation, code workflows, reviews, and quality processes, whilemaintainingengineering standards and accountability.
• Introduceagentic and multi-step AI workflowsresponsibly, with clear guardrails, limits, and validation.
• LeadLLMOpsreadinessfor GenAI-enabled systems, including evaluation baselines, regression testing, drift monitoring, incident response mechanisms, and controlled model or prompt rollout.
• Ensure GenAI features meetenterprise standardsfor reliability, observability, data protection, and risk governance before release.
• Coordinatecross-functional AI dependenciesacross data, security, legal, infrastructure, and engineering to support responsible production adoption.

5) Lead blended teams in a client advisory environment

• Operate as asenior technical delivery leaderacross Bain, client, and partner teams in matrixed environments whilemaintainingengineering credibility.
• Translatesystem-level technical constraints into executive-ready trade-offs and decisions, aligning C-level priorities with engineering realities.
• Builddurable delivery capability within client organizationsby raising the bar on engineering rigor, transparency, and execution discipline.

What We’re Looking For

Core experience

• 7–12+ years of experience in technical program management or large-scale technology deliverywithin complex product, platform, or enterprise environments (industry, consulting, or a blend).
• Demonstrated ownership ofcross-team, multi-system programswith measurable delivery and system-level outcomes.
• Strong technical depth. Comfortable operating with engineers on distributed systems, APIs, reliability targets (SLOs/SLIs), CI/CD, DevOps, and platform architecture.
• Proven ability to serve as asingle-threaded owner for system-leveldeliveryhealth, driving critical-path alignment and resolving cross-team integration risks.
• Experience embeddingnon-functional rigor(performance, scalability, resilience, security) into execution mechanisms and release readiness.
• Ability toinfluence senior technology leaders and executiveswithout formal authority, translating technical constraints into clear, actionable trade-offs.
• Systems-level thinking, including the ability to structure ambiguity, manage dependencies, and balance feature velocity with long-term system health.
• Familiarity with modernAI-enabled engineering workflowsand the operational implications ofLLMOpsand AI risk governance.

Why Bain

• Work on enterprise-scale, mission-critical technology transformations across industries.
• Operate at the intersection of Board/C-level strategy and deep engineering execution.
• Solve high-complexity, cross-domain problems that span architecture, data, platform, AI, and operating model transformation.
• Build and shape a rapidly scaling Technology Delivery capability in APAC and influence how system-level delivery is defined within Bain and with our clients.
• Collaborate with senior consulting leaders, architects, product leaders, and engineers in a high-performance, intellectually rigorous environment.