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About This Role
remote and located in the US and working ET hours
Chatbot Developer – Conversational AI
Role Overview
We are seeking a talented and passionate Conversational AI Developer to play a pivotal role in managing current day chatbot implantations and designing, building, and optimizing our next-generation conversational experiences. In this role, you will bridge the gap between classical NLU systems and modern Generative AI orchestrations. You will be responsible for maintaining our robust enterprise foundations while actively driving our migration strategy toward autonomous agent frameworks.
The ideal candidate possesses a deep technical background in the Microsoft conversational ecosystem, a strong understanding of enterprise integration layers, and a passion for delivering seamless, human-like, yet transparent user experiences.
Key Responsibilities
- End-to-End Development: Drive the full lifecycle development (from conversational design to production deployment) of scalable chatbots, virtual assistants, and AI agents.
- Architecture Migration: Maintain and optimize our current Azure Bot Framework SDK v4 stack while actively participating in the architectural migration toward Microsoft Foundry and Agents 365 Azure SDK.
- Enterprise Integration: Design and implement robust API integrations using MuleSoft as the central enterprise service bus to orchestrate data exchange between conversational front ends and core backend systems, including PeopleSoft and Salesforce.
- Omnichannel Dialog Management: Author and manage rich, dynamic conversational interfaces using Microsoft Dynamics Adaptive Cards, Knowledge Management systems, and Automation Flows across web, mobile, and messaging channels.
- Live Chat and Case Management: Build seamless escalation protocols, including live agent handoffs to Salesforce Live Chat and automated ticket routing via Salesforce Case Management APIs.
- NLU and LLM Orchestration: Integrate Hybrid AI strategies by combining classical NLU (Azure Conversational Language Understanding/CLU) with Large Language Models (Azure OpenAI/GPT) for intelligent intent routing, fallback handling, and contextual content generation.
- Performance Optimization: Analyze conversation logs, containment rates, and intent accuracy metrics using data visualization tools to continuously optimize conversational flows and minimize hallucinations.
- Cross-Functional Collaboration: Partner closely with Product Managers, UX Designers, and business stakeholders to translate complex business logic into intuitive user stories and turn technical constraints into delightful user experiences.
Technical & Professional Qualifications
Required Experience
- 3+ years of dedicated experience in software development with a specialized focus on Conversational AI, NLP/NLU, or LLM agent development.
- Microsoft Bot Stack Expertise: Deep, hands-on proficiency with Azure Bot Framework SDK v4 (including Waterfall Dialogs, custom middleware, and state management) alongside experience or strong theoretical knowledge of Copilot Studio / Agents 365.
- Core Backend Skills: Strong programming fundamentals with advanced proficiency in C#, .NET, and JavaScript, combined with frontend capabilities (HTML/CSS) for custom web-chat styling and Adaptive Card rendering.
- Enterprise Systems Integration: Direct experience working with MuleSoft layers to pull/push data from legacy enterprise platforms like PeopleSoft and CRM systems like Salesforce.
Preferred Qualifications
- Data and State Management: Experience configuring and querying Azure Cosmos DB for storing conversation state, user context, and telemetry logs.
- Analytics Driven: Familiarity utilizing Power BI or Azure Application Insights to analyze drop-off points, intent confusion matrices, and chatbot containment metrics.
- Modern DevOps: Experience defining and maintaining CI/CD pipelines for conversational assets across Dev, Test, and Production environments.
- Industry Context: Experience implementing conversational solutions within Higher Education or complex, multi-stakeholder enterprise environments is a plus.
- Soft Skills: Exceptional communication skills with the ability to articulate technical AI constraints to non-technical business stakeholders.
Benefits & Culture
- Cutting-Edge Innovation: Be part of a forward-thinking team actively reshaping the digital student and staff experience through advanced AI and Agentic workflows.
- Impact-Driven Work: Build tools that directly solve user pain points, handling high-volume transactions with immediate real-world impact.
- Professional Growth: Work in a fast-paced environment that actively supports upskilling in Generative AI, LLM orchestration, and next-generation Microsoft AI technologies.
Notes:
40 hours per week (no overtime)
Remote
VIVA is an equal opportunity employer. All qualified applicants have an equal opportunity for placement, and all employees have an equal opportunity to develop on the job. This means that VIVA will not discriminate against any employee or qualified applicant on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status
Salary Context
This $104K-$114K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At VIVA, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($109K) sits 48% below the category median. Disclosed range: $104K to $114K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
VIVA AI Hiring
VIVA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Austin, TX, US, Remote, US. Compensation range: $114K - $135K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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