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Conversational Ai Developer Jobs in Georgia (NOW HIRING)

CBRE

Senior Principal AI/ML Engineer

Atlanta, GA · On-site

$120K - $166K/yr

Role Summary The Senior AI/ML Engineer is a hands-on technical practitioner responsible for ... Conversational AI Development * Architect and fine-tune intelligent virtual assistants and multi ...

KPMG

Manager, AI Engineer

Atlanta, GA

KPMG is currently seeking a Manager, AI Engineer to join our Advisory Services practice ... Experience in building conversational AI/chatbots, generative AI use cases, or AI-powered ...

Bigcommerce

Senior Backend Engineer (AI Agent)

Atlanta, GA · On-site +1

$116K - $195K/yr

BigCommerce is building a new team to deliver a suite of conversational AI agents spanning the B2B ... As a Senior Backend Engineer specializing in AI agents, you will design the agents themselves ...

KPMG

Senior Associate, AI Engineer

Atlanta, GA · On-site

$53.25 - $68.50/hr

Develop and test AI models, conversational agents, and workflow automation components under the ... Proficiency in Python or another programming language commonly used in AI/ML * Familiarity with ...

KPMG

Senior Associate, AI Engineer

Atlanta, GA · On-site

$53.25 - $68.50/hr

Develop and test AI models, conversational agents, and workflow automation components under the ... Proficiency in Python or another programming language commonly used in AI/ML * Familiarity with ...

Lorven Technologies

Tech Lead (AI)

Alpharetta, GA · On-site

Role: Tech Lead (AI) Location: NYC, NY (3 days onsite in a week) Position Type: Fulltime 12- 15 ... conversational platforms. The ideal candidate combines strong backend engineering skills with the ...

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Conversational Ai Developer information

What are the key skills and qualifications needed to thrive as a conversational AI developer?

To thrive as a Conversational AI Developer, you need a solid background in computer science, natural language processing (NLP), and machine learning, often supported by a relevant degree or certifications. Familiarity with tools like Python, TensorFlow, NLP libraries (such as spaCy or NLTK), and conversational platforms (like Dialogflow or Microsoft Bot Framework) is typically required. Strong problem-solving abilities, creativity, and effective communication skills help developers design intuitive and engaging conversational experiences. These skills are crucial for building AI systems that understand and respond accurately to human language, ensuring user satisfaction and business value.

What is a conversational AI developer?

A Conversational AI Developer is a professional who designs, builds, and maintains artificial intelligence systems that enable computers to interact with humans using natural language. These developers create chatbots, virtual assistants, and voice-enabled applications that can understand and respond to user input in a conversational manner. Their work often involves programming, natural language processing (NLP), machine learning, and integrating AI systems with various platforms or devices. They play a key role in improving user experiences through more intuitive, human-like interactions with technology.

What is the difference between Conversational Ai Developer vs Chatbot Developer?

AspectConversational Ai DeveloperChatbot Developer
Required CredentialsProgramming skills, AI/machine learning knowledge, NLP expertiseProgramming skills, basic AI understanding, scripting
Work EnvironmentTech companies, AI startups, enterprise AI teamsCustomer service, marketing, small to medium businesses
Industry UsageDevelops complex conversational systems with AI capabilitiesBuilds rule-based or simple chatbots for specific tasks
Search & Comparison IntentUnderstanding AI-driven conversational systemsCreating basic automated chat interactions

Conversational Ai Developers focus on building advanced, AI-powered conversational systems using NLP and machine learning, often working in tech or enterprise environments. Chatbot Developers typically create rule-based or simple chatbots for customer service or marketing, with less emphasis on AI complexity. While both roles involve programming and scripting, Conversational Ai Developers require deeper AI and NLP expertise for sophisticated interactions.

What are some common challenges conversational AI developers face when optimizing chatbot performance?

Conversational AI Developers often encounter challenges such as accurately interpreting user intent, handling ambiguous or unexpected queries, and maintaining natural, engaging dialogue flows. Balancing the need for robust natural language understanding with system efficiency and scalability is also key. Additionally, integrating the chatbot with existing platforms and ensuring data privacy can require careful planning and cross-functional collaboration.
What are popular job titles related to Conversational Ai Developer jobs in Georgia? For Conversational Ai Developer jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Conversational Ai Developer jobs in Georgia look for? The top searched job categories for Conversational Ai Developer jobs in Georgia are:
What cities in Georgia are hiring for Conversational Ai Developer jobs? Cities in Georgia with the most Conversational Ai Developer job openings:
Conversational Ai Developer jobs near you
Infographic showing various Conversational Ai Developer job openings in Georgia as of August 2026, with employment types broken down into 75% Full Time, 20% Part Time, 1% Temporary, and 4% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution.

Senior Principal AI/ML Engineer

CBRE

Atlanta, GA • On-site

$120K - $166K/yr

Full-time

Re-posted 3 days ago

CBRE rating

8.1

Company rating: 8.1 out of 10

Based on 349 frontline employees who took The Breakroom Quiz

104th of 488 rated business services

Job description

About CBRE Data & Technology
CBRE is the world's largest commercial real estate services and investment firm. Our Data & Technology organization sits at the intersection of real estate expertise and digital innovation, building the platforms, data products, and AI capabilities that give CBRE and its clients a decisive competitive edge. We are transforming how CBRE operates - embedding advanced AI directly into the business to accelerate delivery, surface insight, and turn product vision into measurable client and revenue impact.
Role Summary
The Senior AI/ML Engineer is a hands-on technical practitioner responsible for designing, building, and operationalizing production-grade AI and machine learning systems that power enterprise intelligence. This is not a research role - it is an engineering role. You own deliverables end-to-end.
This position sits at the center of our most strategic AI initiatives: advancing agentic workflows, developing the enterprise knowledge graph and its underlying ontology, and delivering AI-driven insights that support account intelligence and portfolio analytics programs. You will build reusable, scalable AI capabilities that replace fragmented experiments with durable platform assets.
You are equally at home designing a knowledge graph schema in the morning and shipping fine-tuned model evaluations in the afternoon. You write production code, make principled architecture decisions, and communicate clearly with both engineers and business stakeholders.
What You'll Do
Agentic AI & Workflow Automation
  • Design and implement agentic AI frameworks including multi-agent orchestration, tool-calling pipelines, and autonomous task execution systems.
  • Build and optimize RAG (Retrieval-Augmented Generation) pipelines, prompt chaining workflows, and memory systems that operate reliably at enterprise scale.
  • Integrate LLM-powered agents with internal APIs, databases, and business systems to automate complex, knowledge-intensive workflows.

Enterprise Knowledge Graph & Ontology
  • Contribute to the design and maintenance of the enterprise knowledge graph, including schema design, entity resolution, and relationship modeling.
  • Lead ontology development efforts - defining concepts, hierarchies, and taxonomies that structure enterprise data within the knowledge platform.
  • Integrate semantic models and graph databases with conversational AI and search systems to improve contextual retrieval and reasoning.

Model Engineering & MLOps
  • Design, train, evaluate, and deploy ML models for predictive analytics, classification, anomaly detection, and optimization use cases.
  • Apply domain-specific fine-tuning techniques to align large language models with enterprise knowledge and workflows.
  • Build and maintain ML pipelines using MLOps tooling - ensuring reproducibility, model versioning, drift monitoring, and CI/CD integration.

AI-Driven Insights & Analytics
  • Analyze large, complex datasets to surface actionable trends, patterns, and signals using statistical and machine learning methods.
  • Develop intelligent summarization, extraction, and insight-generation capabilities that convert unstructured data into structured business intelligence.
  • Support account intelligence and portfolio analytics initiatives by building AI-powered features that surface risks, opportunities, and recommendations.

Conversational AI Development
  • Architect and fine-tune intelligent virtual assistants and multi-turn dialogue systems using transformer-based LLMs and enterprise knowledge sources.
  • Design conversation flows, intent hierarchies, and fallback strategies that ensure reliable, high-quality performance across diverse user inputs.

AI Safety, Governance & Quality
  • Identify and mitigate risks in AI/ML systems including hallucination, bias, concept drift, and adversarial vulnerabilities.
  • Implement evaluation frameworks, guardrails, and observability tooling to monitor model quality in production environments.
  • Ensure all AI systems adhere to responsible AI principles and organizational data governance standards.

Collaboration & Communication
  • Partner cross-functionally with data scientists, platform engineers, product managers, and business stakeholders to align AI solutions with strategic objectives.
  • Translate complex technical concepts into clear narratives for non-technical audiences; deliver compelling demos and briefings to senior leaders.
  • Document system architecture, model decisions, and operational runbooks to enable team knowledge-sharing and long-term maintainability.

What You'll Need
  • 5+ years of professional experience in AI/ML engineering, with a proven portfolio of production deployments.
  • Demonstrable track record of shipping AI/ML systems in fast-moving, ambiguous environments.
  • Prior experience working directly with product managers or business stakeholders - not just engineering teams.
  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.

Technical Skills
  • Expert Python proficiency; deep familiarity with ML libraries (scikit-learn, PyTorch, TensorFlow, HuggingFace) and the broader MLOps ecosystem.
  • Proven experience designing and deploying LLM-based systems including RAG architectures, agent frameworks, and fine-tuned models.
  • Hands-on experience with knowledge graph construction, semantic modeling, and graph database technologies.
  • Understanding of ontology development: taxonomy design, entity relationships, and knowledge representation principles.
  • Experience with MLOps practices: model lifecycle management, containerization (Docker/Kubernetes), and CI/CD pipelines.
  • Familiarity with cloud AI/ML platforms (Azure ML, AWS SageMaker, GCP Vertex AI) and associated infrastructure services.

Communication & Collaboration Skills
  • Ability to explain complex technical concepts to non-technical stakeholders clearly and confidently.
  • Natural tendency to document, share, and standardize - you build things others can maintain and extend.
  • Comfort building and presenting demos that generate confidence and drive investment alignment.

Skills Snapshot
  • LLM & Generative AIAgentic WorkflowsKnowledge Graph / Ontology
  • Model Fine-TuningRAG & Vector SearchMLOps & Model Lifecycle
  • Python & ML LibrariesCloud AI PlatformsAI Safety & Governance
  • Data PipelinesAPI Design & IntegrationStakeholder Communication

Why CBRE?
At CBRE, we believe the future of commercial real estate will be built on data and technology. As a Senior AI/ML Engineer, you will be at the center of that transformation - building AI systems that reach real clients, drive measurable business outcomes, and redefine how a global enterprise operates.
  • Named a Fortune Most Admired Real Estate Company for 14 consecutive years.
  • Named a World's Most Ethical Company by Ethisphere for 11 consecutive years.
  • Ranked #3 on Barron's Most Sustainable Company list.
  • Access to a global network of technology, data, and real estate expertise, with investment in AI and digital transformation at the highest levels of leadership.

CBRE carefully considers multiple factors to determine compensation, including a candidate's education, training, and experience. The minimum salary for the Senior Principal AI/ML Engineer position is $235,000.00 annually and the maximum salary for the Senior Principal AI/ML Engineer position is $255,000.00 annually. The compensation that is offered to a successful candidate will depend on the candidate's skills, qualifications, and experience. Successful candidates will also be eligible for a discretionary bonus based on CBRE's applicable benefit program.
Equal Employment Opportunity: CBRE has a long-standing commitment to providing equal employment opportunity to all qualified applicants regardless of race, color, religion, national origin, sex, sexual orientation, gender identity, pregnancy, age, citizenship, marital status, disability, veteran status, political belief, or any other basis protected by applicable law.
Candidate Accommodations: CBRE values the differences of all current and prospective employees and recognizes how every employee contributes to our company's success. CBRE provides reasonable accommodations in job application procedures for individuals with disabilities. If you require assistance due to a disability in the application or recruitment process, please submit a request via email at recruitingaccommodations@cbre.com or via telephone at +1 866 225 3099 (U.S.) and +1 866 388 4346 (Canada).

What CBRE employees say

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Benefits

Hours and flexibility

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About CBRE

Sourced by ZipRecruiter

The real estate industry is undergoing significant and exciting change, increasingly driven by data and technology. At CBRE, the world's premier commercial real estate services company, we empower teams to take ownership over that technology and shape it, offering both nimble, research-driven product design and the resources of a Fortune 500 business. We approach culture with intention, valuing camaraderie, collaboration, inclusivity and a healthy work/life balance. The user experience team is passionate about the quality, usability, and simplicity of the experiences we create. Individuals in these roles gather these key user insights, and then use them to inspire and inform product strategy and design solutions. We partner closely with each other, engineering, and product management to create innovative, usable, great-looking products.

Industry

Real estate

Company size

10,000+ Employees

Headquarters location

Dallas, TX, US

Year founded

1906

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