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About This Role
*We are seeking a* *Senior Full\-stack Engineer* *to join our* *Engineering \& Integration (E\&I)* *team. The ideal candidate blends deep full\-stack development expertise with strong general software engineering skills, experience developing in Salesforce environments, and a commitment to harnessing AI\-augmented development and other cutting\-edge technologies to address complex business challenges.*
*This is an engineering role first. You will write production\-quality code, design scalable platform capabilities, and set the technical standard for how the team builds. Our primary tools and languages span Salesforce, MuleSoft, AWS and other cloud services, Python, and front\-end development.*
*Our team operates at the bleeding edge of IT. We function as a combined Product and Engineering team under the IT umbrella, rewriting the build\-vs\-buy ratio that IT organizations have lived under for decades — building custom, in\-house solutions to problems that are normally reserved for purchased SaaS. We hold ourselves to a high standard of excellence in every product we build and maintain.*
*Central to this role is our* *AI\-SDLC* *methodology — an AI\-native software development lifecycle in which we utilize a variety of AI models in conjunction with AI coding tools as a core part of how we design, build, test, and ship software. We expect you to be fluent with these tools, to stay invested in the rapidly evolving AI landscape, and to actively bring the best of it into the team’s day\-to\-day work.*
*This role is an integral part of the E\&I Engineering Team, collaborating closely with Operations, Finance, Accounting, Sales, Support, Implementation, Product, Engineering, and key stakeholders from other functional units to develop and sustain the processes and data that support Smarsh’s diverse business needs. The team is entrusted with conceiving, implementing, testing, maintaining, and optimizing software solutions that align with the organization’s objectives.*
### Essential Functions
### AI\-SDLC \& Engineering Tooling
- Apply and advance our AI\-SDLC methodology, using AI chat and coding tools as a core part of the development lifecycle — including Claude, ChatGPT, and open\-source alternatives.
- Build and maintain Claude plugins and related tooling that extend AI capabilities into the team’s engineering workflows.
- Build, integrate, and support MCP (Model Context Protocol) servers that connect AI tooling to the team’s systems and data.
- Stay invested in and current with the AI market landscape, continuously evaluating new and best\-in\-class tools and identifying practical ways to implement them in the team’s day\-to\-day work.
- Establish patterns and guardrails for safe, effective, and consistent AI\-assisted development across the team.
### Platform Engineering \& Reusable Capabilities
- Deliver reusable platform capabilities such as unlocked packages, shared Apex and LWC libraries, custom metadata frameworks, and CPQ configuration patterns that reduce duplication, enforce consistency, and accelerate team velocity.
- Design scalable solutions using Salesforce DX, platform events, custom metadata types, and cloud\-specific best practices, creating the “paved path” that domain teams follow to build reliable, governed features.
- Write production\-quality Apex, Lightning Web Components, and integration code. You are expected to contribute directly to the most complex and high\-stakes engineering work, not just review it.
- Develop and maintain integration systems and tooling outside of Salesforce, including REST and SOAP APIs, MuleSoft, and ETL tools.
- Build services and automation in Python or another object\-oriented language where it is the right tool for the job.
### Architecture, Code Quality \& Delivery
- Conduct architecture reviews, solution design sessions, and code reviews for complex implementations across Apex, LWC, Flow, and CPQ, identifying opportunities for shared patterns and platform libraries.
- Apply general software engineering and cloud development best practices, designing, building, and operating solutions on AWS and other cloud services.
- Build and maintain CI/CD pipelines using Salesforce DX, Gearset, GitHub, and other industry\-standard tools, implementing automated testing strategies, schema validation, and deployment automation that reduce friction and enable safe, frequent releases.
- Conduct and participate in code reviews in GitHub, providing and receiving constructive feedback to continuously raise the quality bar.
- Manage and support sandbox and production environments across the enterprise to ensure successful development, testing, and deployment of new features and functionality.
- Maintain version control and branch hygiene, and ensure code coverage through unit testing.
- Investigate and resolve bugs and production issues autonomously.
- Keep abreast of Salesforce releases and effectively leverage relevant updates; support all releases and maintain system uptime in accordance with SLAs.
- Log and track work in a structured ticketing system (Jira).
### Leadership, Mentorship \& Collaboration
- Mentor and coach engineers across teams on Salesforce development, CPQ configuration, architectural patterns, and engineering best practices, raising the technical bar organization\-wide.
- Collaborate cross\-functionally with teams across the entire organization to translate business requirements into scalable platform capabilities.
- Influence the technology roadmap by identifying strategic technical investments, managing technical debt, and aligning platform capabilities with business growth objectives.
- Establish yourself as a primary point of contact for your area of expertise, actively seeking clarification when necessary.
### Education and Experience
- 10\+ years of professional software engineering experience, with at least 5 years focused on the Salesforce platform in complex, multi\-cloud enterprise environments.
- Advanced programming skills in Apex and SOQL/SOSL.
- Competence in Lightning Web Components, HTML, JavaScript, and CSS.
- Proficiency in Python or another object\-oriented programming language.
- Experience with the MuleSoft Integration Platform and DataWeave.
- Experience with AWS and cloud development.
- Demonstrated fluency with AI chat and coding tools (e.g., Claude, ChatGPT, and open\-source alternatives) as part of an AI\-first development workflow.
- Experience building and maintaining Claude plugins (or comparable AI tooling/extensions).
- Experience utilizing and supporting MCP servers.
- A genuine, ongoing investment in the AI market landscape — staying current on new and best\-in\-class tools and a track record of bringing them into team workflows.
- Experience working on a traditional engineering team in close partnership with Product.
- Proficiency in REST and SOAP APIs.
- Proficiency in modern software design and architecture.
- Salesforce Administrator and Salesforce Developer / Platform Developer certifications.
- Experience with DevOps and CI/CD pipelines — GitHub and Gearset.
- Strong analytical, design, and problem\-solving skills.
- Ability to gather and document business requirements effectively.
- Outstanding verbal and written communication skills, with the ability to collaborate effectively within cross\-functional teams.
Additional Eligibility Qualifications
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- Experience with distributed systems and enterprise\-level integrations.
- Experience with container\-based and serverless architectures, including Docker, Kubernetes, and related orchestration tools.
- Experience with general\-purpose languages and frameworks beyond the Salesforce ecosystem (Go, Ruby, JavaScript/Vue.js, Node.js) and cloud\-native development on AWS (Lambda, ECS, Fargate, CodePipeline).
- Salesforce Platform Developer II certification.
- Experience with Salesforce CPQ and Billing.
- Experience implementing Salesforce Experience Cloud.
- Working knowledge of additional high\-level programming languages (C\#, Java, Go, Ruby, etc.).
- Bachelor’s degree in Computer Science, Business, or Information Systems.
The above salary range represents Smarsh's good faith and reasonable estimate of the range of possible base compensation at the time of posting. *Any applicable bonus programs will be discussed during the recruiting process.*
The salary for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, location, specialty and training.
Local cost of living assessments are done for each new hire at the time of offer.
Salary Context
This $147K-$167K 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 Smarsh, 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 ($157K) sits 28% below the category median. Disclosed range: $147K to $167K.
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.
Smarsh AI Hiring
Smarsh has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $167K - $167K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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