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About This Role
The Lead Engineer, Agentic Solutions & Automation will be a key member of the team responsible for advancing agentic platforms and intelligent automation across North American Wealth at Manulife/John Hancock. The successful candidate will play a leading role in driving adoption, owning significant transformation initiatives, establishing reusable platform capabilities and engineering practices, and delivering measurable business outcomes. Working with engineering, architecture, product, operations, risk, compliance, and business partners, this individual will translate strategic objectives into scalable capabilities that move agentic solutions from experimentation into secure, reliable production use. Initial priorities will include advancing an agentic software development lifecycle and improving Developer Experience, while applying the same capabilities to high-value 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 meaningful adoption and impact in a regulated financial services environment.
Position Responsibilities
- Lead strategic initiatives that accelerate the 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 the development and adoption of reusable platform capabilities, reference architectures, patterns, tools, and integrations that enable teams to deliver agents securely and efficiently.
- Lead the establishment and operationalization of engineering standards and governance for agent identity, access, data protection, tool use, human oversight, auditability, evaluation, release controls, and lifecycle management in partnership 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 that enable teams to measure task completion, quality, safety, reliability, latency, cost, adoption, and business value throughout the solution lifecycle.
- Lead technical workshops, labs, demonstrations, and training that 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; make pragmatic recommendations 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.
Required Qualifications
- 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.
Preferred Qualifications
- 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.
The role being advertised is an existing vacancy.
About Manulife and John Hancock
Manulife Financial Corporation is a leading international financial services provider, helping people make their decisions easier and lives better. To learn more about us, visit https://www.manulife.com/en/about/our-story.html.
Manulife is an Equal Opportunity Employer
At Manulife/John Hancock, we embrace our diversity. We strive to attract, develop and retain a workforce that is as diverse as the customers we serve and to foster an inclusive work environment that embraces the strength of cultures and individuals. We are committed to fair recruitment, retention, advancement and compensation, and we administer all of our practices and programs without discrimination on the basis of race, ancestry, place of origin, colour, ethnic origin, citizenship, religion or religious beliefs, creed, sex (including pregnancy and pregnancy-related conditions), sexual orientation, genetic characteristics, veteran status, gender identity, gender expression, age, marital status, family status, disability, or any other ground protected by applicable law.
It is our priority to remove barriers to provide equal access to employment. A Human Resources representative will work with applicants who request a reasonable accommodation during the application process. All information shared during the accommodation request process will be stored and used in a manner that is consistent with applicable laws and Manulife/John Hancock policies. To request a reasonable accommodation in the application process, contact [email protected].
Referenced Salary Location
Boston, Massachusetts Working Arrangement
Hybrid Salary range is expected to be between
$127,330.00 USD - $236,470.00 USD Employees also have the opportunity to participate in incentive programs and earn incentive compensation tied to business and individual performance. The actual salary will vary depending on local market conditions, geography and relevant job-related factors such as knowledge, skills, qualifications, experience, and education/training. If you are applying for this role outside of the primary location, please contact [email protected] for the salary range for your location.
Manulife/John Hancock offers eligible employees a wide array of customizable benefits, including health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage, adoption/surrogacy and wellness benefits, and employee/family assistance plans. We also offer eligible employees various retirement savings plans (including pension/401(k) savings plans and a global share ownership plan with employer matching contributions) and financial education and counseling resources. Our generous paid time off program in the U.S. includes up to 11 paid holidays, 3 personal days, 150 hours of vacation, and 40 hours of sick time (or more where required by law) each year, and we offer the full range of statutory leaves of absence.
We use data and analytics technologies, such as artificial intelligence (AI), and automated processing tools, to analyze and process the information you provide to us or third parties in the application process. For more information, please refer to our personal information collection statement.
Know Your Rights I Family & Medical Leave I Employee Polygraph Protection I Right to Work I E-Verify
Company: John Hancock Life Insurance Company (U.S.A.)
Salary Context
This $127K-$236K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At John Hancock, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($181K) sits 14% below the category median. Disclosed range: $127K to $236K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
John Hancock AI Hiring
John Hancock has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Boston, MA, US. Compensation range: $236K - $236K.
Location Context
AI roles in Boston pay a median of $215,000 across 386 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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