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About This Role
The Senior Forward Deployed Engineer (FDE) is a high impact technical leader responsible for translating the immense power of Talkdesk's Agentic AI platform into transformative, production grade solutions for our strategic enterprise customers. You are a unique blend of a highly experienced software engineer, a technical project lead, and a hands-on builder. You own the technical execution for deployments, mitigate technical risks, and drive successful customer outcomes across assigned projects. This role is for the experienced builder who excels in high stakes, customer facing environments and is passionate about defining the future of Customer Experience Automation.
Responsibilities
- Technical Project Leadership & Architecture: Act as the technical lead for complex deployments. Design and implement the architectural blueprint for AI agent solutions, manage cross-system dependencies, and ensure designs meet stringent enterprise standards for security and scale.
- Hands-On Engineering & Delivery: Write production-grade code and leverage Talkdesk and 3rd-party APIs/SDKs to design, build, test, and deploy AI agents. Drive the execution from prototype through to production deployment, ensuring technical quality.
- Technical Consultation & Alignment: Serve as a trusted technical expert for our AI solutions. Confidently address deep technical inquiries, mitigate technical risk, and build trust with customer Engineering Directors, and technical architects.
- Influence Product & Engineering Roadmap: Synthesize and codify deployment learnings into reusable solution patterns and tooling. Provide actionable feedback to core Product and Engineering teams to help inform future product direction.
- Technical Guidance: Mentor junior FDEs and technical specialists on best practices for complex AI architecture, production quality, and client-facing technical delivery.
Who You Are
We are looking for an autonomous, results-driven technical leader who thrives at the intersection of breakthrough technology and measurable customer value.
Required Qualifications
- Minimum of 5-7 years of progressive experience in software or solution engineering, with at least 1+ years focused on architecting and deploying AI/ML/Agentic solutions.
- Project implementation consulting experience in large scale IT integration projects, ERP projects or AI implementations including experience in client-side project lead experience.
- Strong Experience in Agentic AI System Architecture: Proven experience in the design and end-to-end implementation of complex multi-agent systems, encompassing sophisticated planning, memory, and tool-use frameworks.
- Proficiency in Javascript, Python, Typescript and other relevant languages with a focus on writing secure, scalable, and tested production code.
- Deep expertise in systems integration (APIs, webhooks, message queues) and data architecture to ensure secure, compliant, and performant data flow for the agent across complex customer environments.
- Strong proficiency in prompt engineering, model optimization, and designing systems where agents collaborate to achieve complex outcomes.
- Proven ability to build credibility and consensus with customer executives, lead technical discussions, and drive alignment across internal and external teams.
Preferred Qualifications
- Experience with Retrieval-Augmented Generation (RAG) systems, including hands-on experience with vector databases.
- Experience with health Care Systems such as Epic or Aetna or Retail Services system is a plus.
- Prior experience in the Contact Center (CCaaS), Unified Communications (UCaaS), or Customer Experience (CX) industry.
Pay Range (Base Pay): $180k - $225k
Other Types of Pay: Based on level and role the employee may be eligible for long term incentives in the form of equity and short term incentives of either bonus or commission.
Health Insurance: Medical, Dental, Vision, Life and Disability Insurance, Employee Assistance Program (EAP).
Retirement Benefits: 401(k) plan
Paid Time Off: Talkdesk offers an uncapped paid time off program for exempt employees and an accrual-based program for non-exempt employees; both are subject to manager approval and consistent with business needs.
Paid Holidays: Talkdesk offers 14 paid holidays each year.
Paid Sick Leave: Exempt employees have uncapped paid time off and non-exempt sick leave follows accrual standards; both are subject to manager approval and consistent with business needs.
Method of Application: Apply online.
Application Window: The application window is expected to close at least 120 days from the posting date. The application was posted on 10/22/2026.
Benefits and perks listed above may vary based on the nature of your employment with Talkdesk.
All questions or concerns about this posting should be directed to the Talent team at [email protected].
Talkdesk is pioneering a new era of Customer Experience Automation (CXA), redefining how the world's most admired brands interact with their customers through AI. Our global team of courageous innovators is customer-obsessed, building AI-first solutions that put empathy, trust, and transparency at the center of every interaction. We foster an inclusive culture where diverse perspectives drive our success and every voice belongs. Combining the stability of a global leader with the agility of a disruptor, Talkdeskers are empowered with the autonomy to drive meaningful impact, while giving back to the communities and environment around us.
Talkdesk has been recognized as a Leader in the Gartner® Magic Quadrant™ for Contact Center as a Service (CCaaS) and in the G2 Overall Grid® Reports for AI Agents and Contact Center. With seven consecutive years on the Forbes Cloud 100 and multiple AI Breakthrough awards, there has never been a more exciting time to join us as we shape the future of customer experience automation!
Work Environment and Physical Requirements:
Primarily office-environment work, extended periods of sitting or standing, computer-based work. Limited lifting, and equipment usage limited to computer-related equipment (keyboards, mouse, etc.)
The Talkdesk story hinges on empathy and acceptance. It is the shared goal among all Talkdeskers to empower a new kind of customer hero through our innovative software solution, and we firmly believe that the best path to success for our mission is inclusivity, diversity, and genuine acceptance. To that end, we will hire, promote, work along, cheer for, bond with, and warmly welcome into the Talkdesk family all persons without regard to ethnic and racial identity, indigenous heritage, national origin, religion, gender, gender identity, gender expression, sexual orientation, age, disability, marital status, veteran status, genetic information, or any other legally protected status.
Salary Context
This $180K-$225K 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 Talkdesk, 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. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $180K to $225K.
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.
Talkdesk AI Hiring
Talkdesk has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $225K - $225K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 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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