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About This Role
Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths \- whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.
Bank of America is committed to an in\-office culture that supports collaboration, engagement, and career development. Our approach includes clear in\-office expectations, while providing an appropriate level of flexibility based on role\-specific responsibilities and business needs.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Position Summary:
We are seeking a highly skilled Software Engineer with expert\-level Golang experience, strong system design capabilities, Kubernetes development expertise, and hands\-on exposure to AI\-driven/agentic programming practices.
This role requires someone who can go beyond writing application code — someone who can build intelligent, scalable, enterprise\-ready platform capabilities.
This role is focused on building scalable backend services, platform APIs, Kubernetes\-native automation, and intelligent developer platform capabilities. The ideal candidate is a strong software engineer who can design and build reliable distributed systems in Go, develop software that integrates deeply with Kubernetes, and leverage AI/agentic programming concepts to improve automation, developer productivity, operational workflows, and platform intelligence.
This is not a traditional DevOps, Kubernetes administrator, or basic scripting role. We are looking for an engineer who can write production\-grade Go code, design complex systems, build Kubernetes\-native components, and apply modern AI\-assisted engineering techniques to solve real enterprise platform challenges.
Key Responsibilities:
- Design, develop, and maintain backend services, platform APIs, automation frameworks, and developer\-facing services using Golang.
- Build scalable, resilient, and secure distributed systems that support enterprise platform engineering capabilities.
- Develop Kubernetes\-native components such as controllers, operators, CRDs, admission webhooks, and automation services.
- Build software that integrates with Kubernetes APIs, OpenShift, CI/CD platforms, observability tools, security systems, and enterprise infrastructure services.
- Design API\-first solutions using REST, gRPC, event\-driven patterns, and asynchronous workflows.
- Apply strong system design principles around scalability, resiliency, concurrency, caching, reliability, fault tolerance, and performance optimization.
- Use AI\-assisted and agentic programming approaches to improve engineering productivity, automate repetitive platform tasks, and enhance developer experience.
- Explore and build intelligent automation capabilities such as code analysis agents, remediation workflows, backlog generation, operational assistants, or developer self\-service agents.
- Troubleshoot complex production issues across Go services, Kubernetes workloads, APIs, networking, and distributed systems.
- Participate in architecture reviews and help define engineering standards for Go\-based platform services.
- Collaborate with platform engineering, SRE, security, DevOps, AI engineering, and application teams to deliver reliable enterprise\-scale solutions.
Required Qualifications:
Golang / Backend Engineering
- Strong hands\-on experience developing production\-grade applications in Go / Golang.
- Deep understanding of Go concurrency patterns, goroutines, channels, interfaces, memory management, error handling, context handling, and performance tuning.
- Experience building REST APIs, gRPC services, backend workflows, and event\-driven systems.
- Strong software engineering fundamentals including clean code, testing, modular design, design patterns, dependency management, and maintainability.
- Experience designing and building high\-throughput, low\-latency backend services.
- Ability to debug complex runtime, concurrency, memory, and performance issues in Go applications.
System Design \& Architecture
- Strong system design and architecture skills.
- Ability to design scalable, resilient, fault\-tolerant, and secure distributed systems.
- Strong understanding of:
+ Microservices architecture
+ API design
+ Event\-driven architecture
+ Distributed systems
+ Caching strategies
+ Asynchronous processing
+ Database design
+ Reliability engineering
+ Observability patterns
+ Failure handling and recovery patterns
- Ability to evaluate technical tradeoffs across performance, scalability, security, reliability, maintainability, and delivery timelines.
- Experience taking ambiguous requirements and converting them into clean technical designs and implementation plans.
Kubernetes Development
- Hands\-on experience developing software that integrates with Kubernetes.
- Strong understanding of Kubernetes architecture and core concepts including:
+ Pods
+ Deployments
+ Services
+ Ingress
+ ConfigMaps
+ Secrets
+ Namespaces
+ RBAC
+ CRDs
+ Controllers
+ Operators
+ Admission Controllers / Webhooks
- Experience working with Kubernetes APIs and client libraries, preferably using Go.
- Experience building Kubernetes controllers, operators, automation tooling, or platform extensions.
- Practical experience with Red Hat OpenShift / OCP is strongly preferred.
- Ability to troubleshoot Kubernetes workload, API, networking, and platform integration issues.
AI / Agentic Programming Skills
- Practical experience using AI\-assisted engineering tools and applying AI concepts to software development workflows.
- Understanding of agentic programming concepts, including task planning, tool invocation, workflow automation, context handling, and iterative reasoning loops.
- Experience building or integrating AI\-powered automation, intelligent assistants, code analysis tools, or operational agents is strongly preferred.
- Ability to identify use cases where AI can improve engineering productivity, reduce manual effort, or enhance platform operations.
- Familiarity with LLM\-based application patterns, prompt engineering, retrieval\-augmented generation, tool/function calling, workflow orchestration, or autonomous task execution.
- Experience applying AI in areas such as:
+ Code scanning and remediation
+ Developer self\-service
+ Automated ticket/backlog generation
+ Knowledge extraction
+ Operational troubleshooting
+ Platform support automation
+ Intelligent runbook execution
- Experience with Kubernetes operator development using Kubebuilder, Operator SDK, controller\-runtime, or Kubernetes client\-go.
- Experience with OpenShift platform capabilities including routes, SCCs, operators, cluster integrations, and enterprise platform services.
- Experience with service mesh technologies such as Istio, Consul, or Linkerd.
- Experience with CI/CD and GitOps tools such as Tekton, Argo CD, Jenkins, or GitHub Actions.
- Experience with observability tools such as Prometheus, Grafana, OpenTelemetry, Jaeger, Splunk, or Dynatrace.
- Experience integrating with enterprise security platforms such as Vault, Venafi, IAM, PKI, or secrets management systems.
- Experience with cloud or Kubernetes platforms such as OpenShift, EKS, AKS, Rancher, Tanzu, or GKE.
- Experience with AI frameworks, agent orchestration frameworks, vector search, embeddings, or LLM\-based automation platforms.
- Experience working in financial services or another highly regulated enterprise environment.
Differentiating Skills:
Candidates with the following experience will stand out:
- Expert\-level Golang engineering experience with strong system design depth.
- Built production\-grade backend platforms, APIs, automation frameworks, or developer services.
- Developed Kubernetes controllers, operators, CRDs, or admission webhooks.
- Designed and implemented large\-scale distributed systems.
- Built or contributed to internal developer platforms.
- Integrated Kubernetes with enterprise security, observability, CI/CD, governance, or compliance systems.
- Built AI\-powered engineering tools, agents, automation workflows, or intelligent platform capabilities.
- Strong understanding of how to combine AI, automation, and platform engineering to reduce operational friction.
- Ability to explain complex system design decisions and technical tradeoffs clearly.
Ideal Candidate Profile:
The ideal candidate is a Niche Golang software engineer with strong system design skills, real Kubernetes development experience, and practical exposure to AI/agentic programming.
They should be able to design and build scalable backend services, develop Kubernetes\-native platform components, and use AI\-driven automation to improve developer experience and operational efficiency. This person should think like a software engineer, architect like a systems designer, understand Kubernetes as a development platform, and be curious about how AI can transform platform engineering.
Shift:
1st shift (United States of America)Hours Per Week:
40
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Bank of America, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Bank of America AI Hiring
Bank of America has 8 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer. Positions span Plano, TX, US, New York, NY, US, Pennington, NJ, US. Compensation range: $200K - $232K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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