Lead Engineer, Agentic Solutions & Automation

Manulife•Boston, MA

18h•$127,330 - $236,470•Hybrid

About The Position

The Lead Engineer, Agentic Solutions & Automation will advance agentic platforms and intelligent automation across North American Wealth at Manulife/John Hancock. The role leads adoption, owns significant transformation initiatives, establishes reusable platform capabilities and engineering practices, and delivers measurable business outcomes. Working across engineering, architecture, product, operations, risk, compliance, and business teams, the Lead Engineer translates strategic objectives into scalable capabilities that bring agentic solutions from experimentation into secure, reliable production use. Initial priorities include advancing an agentic software development lifecycle and improving Developer Experience, with opportunities across financial operations, back-office functions, marketing, and advisor and customer journeys. The role combines hands-on engineering, technical leadership, organizational enablement, governance, and accountability for adoption and impact in a regulated financial services environment. The position is a hybrid role based in Boston, Massachusetts. The expected salary range is $127,330 to $236,470 USD. Eligible employees may participate in incentive programs with compensation tied to business and individual performance. Benefits include health, dental, mental health, vision, disability, life and AD&D coverage, adoption/surrogacy and wellness benefits, employee and family assistance plans, retirement savings plans including pension/401(k), a global share ownership plan with employer matching, financial education and counseling resources, paid holidays, personal days, vacation and sick time, and statutory leaves of absence.

Requirements

10+ years of progressive software engineering experience, including technical leadership across complex enterprise platforms or transformation programs.
Demonstrated ability to own and drive complex cross-functional initiatives, influence technical direction, and deliver organizational change through engineering leadership rather than formal authority.
Strong hands-on experience designing and delivering distributed applications, APIs, event-driven systems, and cloud-native services using technologies such as Java, .NET, Python, Node.js, React, and SQL.
Experience establishing production engineering practices across CI/CD, automated testing, security, observability, reliability, and operational readiness.
Experience designing or implementing AI-enabled applications, agents, workflow automation, or LLM-based solutions using retrieval, tool calling, orchestration, and structured outputs.
Working knowledge of AI evaluation, guardrails, privacy, security, human oversight, auditability, and risk controls for regulated environments.
Ability to translate business processes and strategic objectives into reusable technical capabilities, delivery roadmaps, and measurable outcomes.
Strong facilitation and communication skills, including experience leading technical workshops, training, design reviews, and executive-level discussions.
Experience coaching engineers and raising organizational capability through standards, reference implementations, documentation, and communities of practice.
Bachelor's degree in Computer Science, Engineering, or an equivalent combination of education and professional experience.

Nice To Haves

Experience with agent orchestration frameworks and open integration standards such as LangGraph, Microsoft Agent Framework, MCP, A2A, or equivalent technologies.
Experience creating an internal developer platform, paved-road capabilities, reusable agent tooling, or self-service engineering experiences.
Experience with Azure AI services, Azure Kubernetes Service, Azure AI Search, Databricks, GitHub Actions, and enterprise observability platforms.
Strong LLMOps or AgentOps experience, including prompt and model versioning, evaluation datasets, automated quality gates, tracing, telemetry, rollout strategies, and regression management.
Experience establishing AI governance and Responsible AI practices aligned with enterprise risk, security, privacy, compliance, and model-risk expectations.
Experience delivering automation or AI solutions in financial services, particularly wealth management, financial operations, or regulated customer workflows.
Demonstrated success measuring adoption, developer productivity, operational efficiency, quality, risk reduction, or customer outcomes from platform investments.

Responsibilities

Lead strategic initiatives that accelerate adoption of agentic platforms and intelligent automation across North American Wealth, delivering measurable improvements in engineering effectiveness, operational efficiency, and business outcomes.
Own the design and delivery of key components of the agentic SDLC, improving Developer Experience across discovery, requirements, design, development, testing, review, deployment, operations, and continuous improvement.
Partner with engineering, architecture, product, operations, risk, compliance, and business teams to identify, prioritize, and deliver high-value agent use cases across financial operations, back-office functions, marketing, and advisor and customer journeys.
Drive development and adoption of reusable platform capabilities, reference architectures, patterns, tools, and integrations that enable teams to deliver agents securely and efficiently.
Establish and operationalize engineering standards and governance for agent identity, access, data protection, tool use, human oversight, auditability, evaluation, release controls, and lifecycle management with architecture, risk, security, and compliance stakeholders.
Design and implement production-grade agentic systems using orchestration, retrieval, structured tool use, event-driven integration, and human-in-the-loop patterns.
Establish and evolve evaluation, observability, and operational practices to measure task completion, quality, safety, reliability, latency, cost, adoption, and business value throughout the solution lifecycle.
Lead technical workshops, labs, demonstrations, and training to build practical agentic engineering capability across development teams and business partners.
Coach and mentor engineers, build communities of practice, and raise organizational capability as teams move from proofs of concept to governed production solutions.
Evaluate models, frameworks, platforms, and vendors; recommend options based on capability, interoperability, security, cost, performance, and strategic fit.
Influence technology and portfolio direction through prototypes, technical assessments, architecture decisions, and data-driven recommendations to senior engineering and business leaders.