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About This Role
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The Lead, Network Engineer designs, analyzes, plans and modifies network components supporting customer communication implementation activities. The Lead, Network Engineer works on problems of diverse scope and complexity ranging from moderate to substantial.
The Lead Network Engineer will be the technical SME for our enterprise WAN, remote LAN/WLAN, and data center SDWAN network services. This role is responsible for engineering, implementing, and operating a multi\-vendor SD\-WAN environment, scalable campus/branch switching and wireless, and resilient remote branch solutions. You will serve as the escalation point for complex routing, overlay, and authentication issues, including RADIUS/TACACS\+. Additionally, you will provide technical leadership to a team of network engineers. You will also work closely with the Lead Network Architect and Principal Network Architect through structured technical solution peer reviews.
Responsibilities
Engineering \& Design
- Lead the end\-to\-end design of multi\-vendor SD\-WAN solutions (Cisco SD\-WAN/Viptela, Meraki, Fortinet, Aruba, Palo Alto, etc.) across global branch and remote office locations.
- Design and maintain secure and scalable data center solutions to terminate SD\-WAN overlays, VPNs, and remote\-site connectivity.
- Develop robust WAN, campus, and data center routing designs using BGP and OSPF, including route redistribution, policy\-based routing, and traffic engineering.
- Design resilient, standardized remote LAN solutions (access/distribution) including VLANs, spanning\-tree strategy, link aggregation, and QoS.
- Build and test enterprise wireless solutions for remote and campus locations, including SSID design, authentication/authorization policies, RF design standards, and guest access.
- Own the design and lifecycle of RADIUS and TACACS\+ solutions for network device access control, 802\.1X, and Wi\-Fi authentication, including high availability and redundancy.
- Collaborate with the Lead Network Architect and Principal Network Architect to ensure solution designs align with enterprise architecture principles, target\-state roadmaps, and security standards.
Implementation \& Operations
- Lead the deployment and configuration of SD\-WAN overlays, routing policies, segmentation, and security policies across multiple platforms.
- Oversee implementation of switching, routing, and wireless solutions for new sites, data center expansions, and network refresh projects.
- Serve as the highest\-level escalation point for complex incidents involving SD\-WAN overlays, routing instability, LAN/WLAN issues, and AAA (RADIUS/TACACS\+) failures.
- Define and maintain network configuration standards, templates, and golden configurations for SD\-WAN, LAN, WLAN, and routing.
- Collaborate closely with security, platform, and applications teams to ensure network designs meet security, performance, and availability requirements.
Governance, Peer Review \& Documentation
- Participate in structured technical solution peer reviews with the Lead Network Architect and Principal Network Architect, presenting low\-level designs (LLDs), implementation approaches, and migration plans for feedback and approval.
- Review and provide feedback on designs produced by other engineers to ensure consistency with standard guidelines and operational best practices.
- Develop and maintain detailed network diagrams, design documents, and runbooks for SD\-WAN, LAN/WLAN, data center, and AAA infrastructure.
- Contribute to or lead network automation efforts (e.g., Ansible, Python, APIs) to streamline configuration, compliance checks, and deployments across SD\-WAN and LAN.
- Define monitoring and observability standards (NetFlow/sFlow, SNMP, syslog, telemetry) for all network domains; work with NOC/operations on alerting and dashboards.
- Assist in capacity planning, performance analysis, and continual service improvement for WAN, LAN/WLAN, and data center connectivity.
Leadership \& Collaboration
- Provide technical leadership and mentorship to network engineers and operations staff; review designs and changes for quality and consistency.
- Work in close partnership with the Lead Network Architect and Principal Network Architect to translate high\-level architecture into implementable, supportable solutions.
- Participate in and sometimes lead cross\-functional solution reviews and present to the change advisory board (CAB).
- Engage with vendors and service providers for solution evaluation, design improvements, and problem escalation.
- Contribute to network strategy and roadmap planning, identifying opportunities for modernization, consolidation, and cost optimization.
- Ability to translate business requirements into scalable, supportable network designs.
- Strong documentation, communication, and presentation skills; comfortable discussing designs with both technical and non\-technical stakeholders.
- Leadership mindset with the ability to guide, mentor, and influence other engineers.
- High degree of ownership, initiative, and accountability in a fast\-moving environment.
- A minimum standard speed for optimal performance of 25x10 (25mpbs download x 10mpbs upload) is required.
- Satellite and Wireless Internet service is NOT allowed for this role.
- A dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information
Use your skills to make an impact
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Required Qualifications
- Bachelor's degree in computer science, Information Technology, Engineering, or equivalent experience.
- Ability to pass a background investigation to obtain an ADP II Security Clearance.
- 7\+ years of hands\-on experience in enterprise network engineering, with at least 3 years in a senior or lead role.
- Proven experience designing, deploying, and supporting more than one enterprise\-scale SD\-WAN platform (Cisco SD\-WAN/Viptela, Meraki SD\-WAN, Fortinet Secure SD\-WAN, Aruba EdgeConnect, etc.).
- Strong expertise in dynamic routing protocols:BGP: eBGP/iBGP design, route filtering, communities, path manipulation, and troubleshooting.OSPF: multi\-area design, route summarization, LSA types, and convergence tuning.
- Solid experience with enterprise switching:VLANs, 802\.1Q trunking, STP/RSTP/MST, EtherChannel/LACP, and QoS.
- Experience with enterprise wireless networking:Centralized controllers or cloud\-managed Wi\-Fi, RF fundamentals, roaming, guest and secure SSIDs, and 802\.1X\-based authentication.
- Hands\-on experience in designing, deploying, and supporting RADIUS and TACACS\+ solutions (e.g., Cisco ISE, Forescout, ClearPass, or similar) for device admin and network access control.
- Experience working with Infoblox (DDI) and remote site IP addressing design.
- Strong troubleshooting skills across SD\-WAN overlay/underlay, routing, and AAA\-related issues using tools such as packet captures, flow data, and detailed logs.
- Experience working in mission\-critical, highly available network environments.
- Experience working with Data Center Colocation facilities and virtual network fabric provided by vendors such as Equinix and Mega\-Port.
- 2 or more years of project leadership experience
- Must be passionate about contributing to an organization focused on continuously improving consumer experiences
Preferred Qualifications
- Relevant certifications such as:Cisco: CCNP Enterprise, CCIE Enterprise Infrastructure or equivalent level of expertise.Other vendors: NSE (Fortinet), Aruba/HP networking, or equivalent.
- Practical experience leveraging AI tools in a network engineering context, such as:Claude, Gemini, GitHub Copilot (or similar tools) for design assistance, documentation generation, troubleshooting guides, and code review of network automation scripts.
- Experience with agentic AI and automation patterns, including:Designing and working with AI agents, orchestration frameworks, or MCP\-style gateways to interact with network APIs, monitoring platforms, and ticketing systems.We build or maintain Infrastructure as Code (IaC) for network configurations using tools like Terraform, Ansible, or similar ones. We integrate network automation and validation into CI/CD pipelines, such as GitHub Actions, GitLab CI, or Azure DevOps, for purposes like configuration testing, compliance checks, and staged rollouts.
- Familiarity with zero\-trust networking concepts, segmentation, and integration with firewalls and security services (Zscaler ZIA, ZPA).
- Experience in hybrid cloud connectivity (AWS/Azure/GCP) and integrating SD\-WAN with cloud on\-ramps.
- Background in strict change control operations and change management processes.
Additional Information
- Implement (perform build/deploy) \- with appropriate change ticketing/coordination;
- Perform post implementation validation/verification and performance testing \- document results and provide to Humana; Participate in On\-Call rotation;
- Perform after\-hours solutions implementation;
- Support other teams as needed, after\-hours;
Work\-At\-Home Requirements
- WAH requirements: Must have the ability to provide a high speed DSL or cable modem for a home office. Associates or contractors who live and work from home in the state of California will be provided payment for their internet expense.
- A minimum standard speed for optimal performance of 25x10 (25mpbs download x 10mpbs upload) is required.
- Satellite and Wireless Internet service is NOT allowed for this role.
- A dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information
Work at Home Requirements To ensure Home or Hybrid Home/Office employees’ ability to work effectively, the self\-provided internet service of Home or Hybrid Home/Office employees must meet the following criteria: At minimum, a download speed of 25 Mbps and an upload speed of 10 Mbps is required; wireless, wired cable or DSL connection is suggested. In certain roles, the minimum recommended internet speed required by Humana may not be sufficient for business needs. Humana reserves the right to require associates to upgrade their internet service if necessary. Work from a dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information. Travel: While this is a remote position, occasional travel to Humana's offices for training or meetings may be required. Scheduled Weekly Hours
40Pay Range
The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc.
$117,600 \- $161,700 per year
This job is eligible for a bonus incentive plan. This incentive opportunity is based upon company and/or individual performance.Description of Benefits
Humana, Inc. and its affiliated subsidiaries (collectively, “Humana”) offers competitive benefits that support whole\-person well\-being. Associate benefits are designed to encourage personal wellness and smart healthcare decisions for you and your family while also knowing your life extends outside of work. Among our benefits, Humana provides medical, dental and vision benefits, 401(k) retirement savings plan, time off (including paid time off, company and personal holidays, paid parental and caregiver leave), short\-term and long\-term disability, life insurance and many other opportunities.About us
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About Humana: Humana Inc. (NYSE: HUM) is a leading U.S. healthcare company. Through our Humana insurance services and our CenterWell healthcare services, we make it easier for the millions of people we serve to achieve their best health – delivering the care and service they need, when they need it. These efforts are leading to a better quality of life for people with Medicare and Medicaid, families, individuals, military service personnel, and communities at large. Learn more about what we offer at Humana.com and at CenterWell.com.
Equal Opportunity Employer
It is the policy of Humana not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status. It is also the policy of Humana to take affirmative action, in compliance with Section 503 of the Rehabilitation Act and VEVRAA, to employ and to advance in employment individuals with disability or protected veteran status, and to base all employment decisions only on valid job requirements. This policy shall apply to all employment actions, including but not limited to recruitment, hiring, upgrading, promotion, transfer, demotion, layoff, recall, termination, rates of pay or other forms of compensation and selection for training, including apprenticeship, at all levels of employment.
Salary Context
This $117K-$161K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Humana, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($139K) sits 36% below the category median. Disclosed range: $117K to $161K.
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.
Humana AI Hiring
Humana has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Frisco, TX, US. Compensation range: $147K - $208K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 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).
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 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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