LLM Engineer

• Design and build the end-to-end voice conversation pipeline between players and NPCs
• Evaluate and integrate speech-to-text, large language model, text-to-speech, and speech-to-speech technologies
• Evaluate approaches to conversational memory, context, personality, and NPC behavior
• Prototype real-time conversational interactions and optimize latency, quality, reliability, and cost
• Determine which components should run on-device versus in the cloud
• Build and integrate the resulting systems into Unity/C#
• Establish an architecture that can evolve from a prototype into a production system
• Work closely with game design and engineering teams to define technical needs and create natural, responsive player interactions
• Choose the technology stack and justify choices with measured performance, cost, and failure-rate data
• Design and build a working prototype of the conversational AI system in Unity

Conversational AI Engineer – Game Prototype

• Design and operate cloud data platform components, including lakehouse storage, compute, SQL and ELT engines, and reusable transformation workflows using Databricks or comparable platforms
• Develop production Python and SQL code, versioned reusable packages, APIs, and configuration-driven frameworks with automated tests and documentation
• Own CI/CD for data pipelines, AI applications, and supporting services, including environment configuration, automated validation, deployment, release verification, and rollback
• Implement infrastructure as code and secure deployment patterns in AWS, AWS GovCloud, Azure, Azure Government, or other approved environments
• Own ingestion, transformation, and normalization pipelines for structured, semi-structured, and unstructured healthcare and public health data, including batch and streaming workloads
• Build reusable connectors, data contracts, and curated datasets using Python, SQL, Spark or PySpark, Delta Lake, dbt, or comparable technologies
• Integrate Medicaid and Medicare claims and encounters, FHIR R4, US Core, HL7 v2, social determinants of health, geospatial data, surveys, and qualitative sources as projects require
• Translate analytics, reporting, evaluation, and data science requirements into tested data models and dependable datasets with documented lineage
• Define and implement data quality rules, validation, lineage, metadata, and monitoring
• Diagnose and resolve production failures, data quality issues, and performance bottlenecks; implement root-cause fixes and maintain operational runbooks
• Establish reliability and freshness targets; own monitoring, incident response, and recovery for assigned services
• Optimize compute, storage, partitioning, and pipeline execution; track cost and performance and implement guardrails
• Own AI application delivery from requirements and solution design through implementation, evaluation, deployment, and production support
• Build retrieval-augmented generation and document-intelligence applications, including ingestion, chunking, embeddings, vector search, and retrieval
• Use MLflow or comparable tools to manage generative AI evaluation and tracing, evaluation datasets, custom scorers, and prompt/model versioning
• Develop LLM applications and agentic workflows using Python, LangChain, LlamaIndex, or comparable frameworks
• Deploy and operate AI applications and model endpoints using Databricks Model Serving or comparable services, with governed data access, monitoring, and CI/CD
• Build reusable AI services and automation for healthcare reporting, document extraction, and knowledge retrieval
• Apply privacy by design, encryption, least-privilege access, secrets management, and secure networking
• Implement technical controls and documentation for HIPAA, 42 CFR Part 2, IRB and data-use agreement restrictions, and responsible AI practices informed by the NIST AI Risk Management Framework
• Document model behavior, evaluate fairness and explainability, and maintain model cards and governance artifacts
• Serve as technical lead for defined projects or workstreams; coordinate contributors, review implementation choices, and resolve blockers
• Mentor colleagues through pairing, code reviews, and technical guidance; lead workshops and create tutorials, runbooks, and examples
• Explain technical concepts and tradeoffs to varied audiences and contribute technical materials for proposals and client engagements

Data Engineer – Generative AI, Healthcare Data

• Architect conversational AI systems that transform how clients and advisors interact with Farther’s wealth management platform
• Own conversational AI features end-to-end, from requirements through production deployment
• Architect chatbot systems using LLMs, RAG pipelines, and agentic workflows
• Build production systems with testing, observability, reliability, and redundancy
• Develop guardrails and authorization boundaries for AI agents handling sensitive financial data
• Troubleshoot production issues, complex prompt engineering challenges, and unfamiliar model behaviors
• Collaborate directly with product, data engineering, and business stakeholders
• Make architectural decisions and own outcomes

Senior Conversational AI Engineer

• Drive the design, development, and operationalization of advanced large-language-model capabilities across a cloud-based analytics ecosystem
• Lead innovation efforts around AI, including architecture and strategy for fine-tuning, retrieval-augmented generation (RAG), agentic frameworks, and domain-specific model adaptation
• Guide development of high-impact prototypes
• Oversee the evolution of scalable LLM pipelines
• Ensure governance, security, and performance across model implementations
• Partner with engineering, product, and data teams
• Provide technical leadership and evaluate emerging LLM technologies
• Set best practices and help drive practical, safe, and effective deployment of generative AI

Large Language Model Specialist, AWS Cloud

• Design, build, test, and maintain NICE virtual agents focused on digital chat and self-service
• Develop and manage conversation flows, intents, entities, dialog logic, and live-agent handoff scenarios across voice and digital channels
• Configure and tune NLU models to improve intent recognition, accuracy, and customer experience
• Implement and maintain integrations between Cognigy and CRM systems, knowledge bases, APIs, and automation services
• Support human handoff and omnichannel continuity while preserving context
• Monitor and optimize bot performance using analytics, transcripts, and interaction data
• Troubleshoot and resolve production issues involving bot behavior, NLU accuracy, integrations, and platform performance
• Maintain technical documentation for conversation designs, configurations, and integrations
• Collaborate with Business Analysts, CX designers, product owners, and stakeholders in discovery and design workshops
• Participate in design reviews, backlog refinement, and release planning
• Communicate bot capabilities, limitations, and release readiness to technical and non-technical partners
• Follow change management, testing, and documentation standards for chatbot deployments
• Support production releases and critical after-hours issues
• Identify opportunities to expand self-service and automation use cases

Conversational AI Engineer

• Develop, configure and deploy conversational AI solutions using the Kore.ai platform
• Build and enhance chatbots and AI agents using Kore.ai XO10, XO11 or Artemis
• Design and implement Agentic AI solutions using LLMs, RAG and Prompt Engineering
• Develop integrations with enterprise applications using REST APIs, JSON, JavaScript and Node.js
• Design and implement reusable conversational AI components and agent capabilities
• Integrate LLMs, Vector Databases and enterprise data sources into AI solutions
• Implement multi-agent orchestration and AI observability practices where required
• Troubleshoot production issues and improve AI response accuracy, stability and performance
• Ensure solutions comply with security, governance and enterprise architecture standards
• Collaborate with business, technology and product teams to deliver conversational AI use cases

Associate Staff Engineer – Conversational AI Developer

• Drive the design, development, and operationalization of advanced large-language-model capabilities across a cloud-based analytics ecosystem
• Lead innovation efforts around cutting-edge AI
• Own architecture and strategy for fine-tuning, retrieval-augmented generation (RAG), agentic frameworks, and domain-specific model adaptation
• Guide development of high-impact prototypes
• Oversee evolution of scalable LLM pipelines
• Ensure robust governance, security, and performance across model implementations
• Partner with engineering, product, and data teams
• Provide technical leadership and evaluate emerging LLM technologies
• Set best practices and help drive transformation through practical, safe, and effective deployment of generative AI

Large Language Model Specialist – AWS Cloud

• Lead hands-on development and delivery of intelligent operations solutions, including advanced chatbots, agentic workflow automation, and AI-assisted decision platforms
• Fine-tune, prompt-engineer, and deploy open-source and proprietary large language models via Google Vertex AI
• Implement Retrieval-Augmented Generation pipelines
• Architect and implement AI safety frameworks, guardrails, and deterministic controls
• Design and deploy scalable AI pipelines using Python, Docker, Kubernetes, and cloud-native architectures
• Apply financial services technology expertise to ensure AI models meet risk management, auditability, and financial data security standards
• Set technical direction and architect governance frameworks for senior-level AI programs

Lead Generative AI Engineer, VP

• Design challenging, domain-relevant tasks across GPU kernels, performance profiling, debugging, and inference serving, and write accurate, well-structured solutions
• Guide research and engineering teams to close knowledge gaps and improve AI model performance on ML systems, training infrastructure, and framework-level topics
• Evaluate MLOps and ML systems tasks and solutions and provide clear, written technical feedback
• Develop guidelines and detailed rubrics or evaluation frameworks covering kernel-level optimization, profiler output interpretation, distributed systems reasoning, and serving throughput and latency trade-offs
• Collaborate with subject matter experts to keep training data consistent and accurate
• Contribute to AI model training and evaluation work by writing and assessing MLOps and ML systems tasks and solutions for frontier AI training data

MLOps Engineer, LLM Systems, Serving, GPU Kernels, Profiling

• Design dynamic, non-scripted conversation flows for outbound sales/follow-up calls and a support/sales chatbot
• Implement intent handling, objection handling, and human escalation logic
• Build chatbot knowledge-base structures and content-update processes
• Transcribe and evaluate existing Zoom Phone/AWS calls
• Score tone, sentiment, professionalism, and compliance against defined QA criteria
• Integrate call/chat outcomes, transcripts, and summaries with GoHighLevel and/or CRM systems
• Design for future portability across 8x8, Vonage, RingCentral, and Ooma
• Support future client-facing deployment
• Maintain restricted access and data-protection standards for customer call data
• Own execution end to end within a solo, fixed-scope engagement
• Participate in IT touchpoints and management check-ins as needed to review progress

LLM Engineer jobs in United States