Principal Agentic AI Chatbot Engineer
Amgen
Principal Agentic AI Chatbot Engineer
Job Description
Join Amgen’s Mission of Serving Patients
What you will do
- Own the enterprise agent/bot platform architecture (cloud/on-prem), API contracts, and guardrails for multi-tenant use
- Design and ship production agentic systems: multi-agent planning, tool/function calling, workflow orchestration, and structured outputs
- Build high-containment bots for web/mobile/Slack/Teams/IVR with streaming responses and sub-second turn latency where required
- Implement retrieval & memory (RAG, vector stores, knowledge graphs, session memory) with data contracts, lineage, and lifecycle governance
- Establish bot CI/CD: prompt & config versioning, replay/conversation tests, feature flags, and automated rollbacks. Implement progressive delivery (blue-green, canary) with health checks, feature flags, and one-click rollback
- Stand up observability: tracing, metrics, and conversation analytics (containment, escalation, hallucination, CSAT, latency, cost per turn) with alerts and runbooks
- Integrate securely with enterprise systems (e.g., SAP, Salesforce, ServiceNow, MES/LIMS/ELN/QMS) via robust connectors and least-privilege tool access
- Embed safety, privacy, and compliance (content filtering, red-teaming, audit trails, secrets mgmt) aligned to GxP/21 CFR Part 11 and HIPAA/PII needs
- Optimize inference and cost (routing, caching, batching, distillation/quantization) and drive GPU/accelerator/FinOps strategy
- Evaluate and document trade-offs for models (open vs. proprietary), hosting options, and agent frameworks (e.g., LangChain, Semantic Kernel, Assistants APIs)
- Translate domain needs (R&D, Manufacturing, Commercial) into prioritized roadmaps; mentor teams and communicate technical trade-offs to stakeholders
What we expect of you
- Doctorate degree and 2 years of Computer Science, IT or related field experience
- Or Master’s degree and 4 years of Computer Science, IT or related field experience
- Or Bachelor’s degree and 6 years of Computer Science, IT or related field experience
- Or Associate’s degree and 10 years of Computer Science, IT or related field experience
- Or High school diploma / GED and 12 years of Computer Science, IT or related field experience
- Preferred: 3-5 years in AI/ML and enterprise software
- Knowledge graphs or structured reasoning (SPARQL, RDF/OWL) and tool-use planning with constraints
- Safety tooling (jailbreak detection, content moderation, PII redaction) and automated red-team pipelines
- Experience with privacy-preserving patterns (data masking, synthetic data, federated or on-prem inference)
- Domain familiarity in biopharma workflows (clinical operations, quality, manufacturing/supply chain, field/commercial)
- FinOps at scale: GPU fleet planning, utilization dashboards, commitment management
- Contributions to open source in LLM/agent ecosystems; patents or publications in conversational AI
- Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives
- Good-to-Have: Experience in Biotechnology or pharma industry
- Published thought-leadership or conference talks on enterprise GenAI adoption
- Master’s degree in Computer Science and or Data Science
- Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery
- Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.)
Must-Have Skills
- Excellent analytical and troubleshooting skills
- Strong verbal and written communication skills
- Ability to work effectively with global, virtual teams
- High degree of initiative and self-motivation
- Ability to manage multiple priorities successfully
- Team-oriented, with a focus on achieving team goals
- Ability to learn quickly, be organized and detail oriented
- Strong presentation and public speaking skills
What you can expect of us
- Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
- Group medical, dental and vision coverage
- Life and disability insurance
- Flexible spending accounts
- Discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
- Stock-based long-term incentives
- Award-winning time-off plans
- Flexible work models, including remote and hybrid work arrangements, where possible
Compensation
3-5
Locations
- Thousand Oaks, United States of America (Remote)
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
180,000 - 220,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Excellent analytical and troubleshooting skillsintermediate
- Strong verbal and written communication skillsintermediate
- Ability to work effectively with global, virtual teamsintermediate
- High degree of initiative and self-motivationintermediate
- Ability to manage multiple priorities successfullyintermediate
- Team-oriented, with a focus on achieving team goalsintermediate
- Ability to learn quickly, be organized and detail orientedintermediate
- Strong presentation and public speaking skillsintermediate
Required Qualifications
- Doctorate degree and 2 years of Computer Science, IT or related field experience (experience)
- Or Master’s degree and 4 years of Computer Science, IT or related field experience (experience)
- Or Bachelor’s degree and 6 years of Computer Science, IT or related field experience (experience)
- Or Associate’s degree and 10 years of Computer Science, IT or related field experience (experience)
- Or High school diploma / GED and 12 years of Computer Science, IT or related field experience (experience)
- Preferred: 3-5 years in AI/ML and enterprise software (experience)
- Knowledge graphs or structured reasoning (SPARQL, RDF/OWL) and tool-use planning with constraints (experience)
- Safety tooling (jailbreak detection, content moderation, PII redaction) and automated red-team pipelines (experience)
- Experience with privacy-preserving patterns (data masking, synthetic data, federated or on-prem inference) (experience)
- Domain familiarity in biopharma workflows (clinical operations, quality, manufacturing/supply chain, field/commercial) (experience)
- FinOps at scale: GPU fleet planning, utilization dashboards, commitment management (experience)
- Contributions to open source in LLM/agent ecosystems; patents or publications in conversational AI (experience)
- Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives (experience)
- Good-to-Have: Experience in Biotechnology or pharma industry (experience)
- Published thought-leadership or conference talks on enterprise GenAI adoption (experience)
- Master’s degree in Computer Science and or Data Science (experience)
- Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery (experience)
- Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) (experience)
Responsibilities
- Own the enterprise agent/bot platform architecture (cloud/on-prem), API contracts, and guardrails for multi-tenant use
- Design and ship production agentic systems: multi-agent planning, tool/function calling, workflow orchestration, and structured outputs
- Build high-containment bots for web/mobile/Slack/Teams/IVR with streaming responses and sub-second turn latency where required
- Implement retrieval & memory (RAG, vector stores, knowledge graphs, session memory) with data contracts, lineage, and lifecycle governance
- Establish bot CI/CD: prompt & config versioning, replay/conversation tests, feature flags, and automated rollbacks. Implement progressive delivery (blue-green, canary) with health checks, feature flags, and one-click rollback
- Stand up observability: tracing, metrics, and conversation analytics (containment, escalation, hallucination, CSAT, latency, cost per turn) with alerts and runbooks
- Integrate securely with enterprise systems (e.g., SAP, Salesforce, ServiceNow, MES/LIMS/ELN/QMS) via robust connectors and least-privilege tool access
- Embed safety, privacy, and compliance (content filtering, red-teaming, audit trails, secrets mgmt) aligned to GxP/21 CFR Part 11 and HIPAA/PII needs
- Optimize inference and cost (routing, caching, batching, distillation/quantization) and drive GPU/accelerator/FinOps strategy
- Evaluate and document trade-offs for models (open vs. proprietary), hosting options, and agent frameworks (e.g., LangChain, Semantic Kernel, Assistants APIs)
- Translate domain needs (R&D, Manufacturing, Commercial) into prioritized roadmaps; mentor teams and communicate technical trade-offs to stakeholders
Benefits
- general: Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
- general: Group medical, dental and vision coverage
- general: Life and disability insurance
- general: Flexible spending accounts
- general: Discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
- general: Stock-based long-term incentives
- general: Award-winning time-off plans
- general: Flexible work models, including remote and hybrid work arrangements, where possible
Tags & Categories
Principal Agentic AI Chatbot Engineer
Amgen
Principal Agentic AI Chatbot Engineer
Job Description
Join Amgen’s Mission of Serving Patients
What you will do
- Own the enterprise agent/bot platform architecture (cloud/on-prem), API contracts, and guardrails for multi-tenant use
- Design and ship production agentic systems: multi-agent planning, tool/function calling, workflow orchestration, and structured outputs
- Build high-containment bots for web/mobile/Slack/Teams/IVR with streaming responses and sub-second turn latency where required
- Implement retrieval & memory (RAG, vector stores, knowledge graphs, session memory) with data contracts, lineage, and lifecycle governance
- Establish bot CI/CD: prompt & config versioning, replay/conversation tests, feature flags, and automated rollbacks. Implement progressive delivery (blue-green, canary) with health checks, feature flags, and one-click rollback
- Stand up observability: tracing, metrics, and conversation analytics (containment, escalation, hallucination, CSAT, latency, cost per turn) with alerts and runbooks
- Integrate securely with enterprise systems (e.g., SAP, Salesforce, ServiceNow, MES/LIMS/ELN/QMS) via robust connectors and least-privilege tool access
- Embed safety, privacy, and compliance (content filtering, red-teaming, audit trails, secrets mgmt) aligned to GxP/21 CFR Part 11 and HIPAA/PII needs
- Optimize inference and cost (routing, caching, batching, distillation/quantization) and drive GPU/accelerator/FinOps strategy
- Evaluate and document trade-offs for models (open vs. proprietary), hosting options, and agent frameworks (e.g., LangChain, Semantic Kernel, Assistants APIs)
- Translate domain needs (R&D, Manufacturing, Commercial) into prioritized roadmaps; mentor teams and communicate technical trade-offs to stakeholders
What we expect of you
- Doctorate degree and 2 years of Computer Science, IT or related field experience
- Or Master’s degree and 4 years of Computer Science, IT or related field experience
- Or Bachelor’s degree and 6 years of Computer Science, IT or related field experience
- Or Associate’s degree and 10 years of Computer Science, IT or related field experience
- Or High school diploma / GED and 12 years of Computer Science, IT or related field experience
- Preferred: 3-5 years in AI/ML and enterprise software
- Knowledge graphs or structured reasoning (SPARQL, RDF/OWL) and tool-use planning with constraints
- Safety tooling (jailbreak detection, content moderation, PII redaction) and automated red-team pipelines
- Experience with privacy-preserving patterns (data masking, synthetic data, federated or on-prem inference)
- Domain familiarity in biopharma workflows (clinical operations, quality, manufacturing/supply chain, field/commercial)
- FinOps at scale: GPU fleet planning, utilization dashboards, commitment management
- Contributions to open source in LLM/agent ecosystems; patents or publications in conversational AI
- Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives
- Good-to-Have: Experience in Biotechnology or pharma industry
- Published thought-leadership or conference talks on enterprise GenAI adoption
- Master’s degree in Computer Science and or Data Science
- Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery
- Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.)
Must-Have Skills
- Excellent analytical and troubleshooting skills
- Strong verbal and written communication skills
- Ability to work effectively with global, virtual teams
- High degree of initiative and self-motivation
- Ability to manage multiple priorities successfully
- Team-oriented, with a focus on achieving team goals
- Ability to learn quickly, be organized and detail oriented
- Strong presentation and public speaking skills
What you can expect of us
- Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
- Group medical, dental and vision coverage
- Life and disability insurance
- Flexible spending accounts
- Discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
- Stock-based long-term incentives
- Award-winning time-off plans
- Flexible work models, including remote and hybrid work arrangements, where possible
Compensation
3-5
Locations
- Thousand Oaks, United States of America (Remote)
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
180,000 - 220,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Excellent analytical and troubleshooting skillsintermediate
- Strong verbal and written communication skillsintermediate
- Ability to work effectively with global, virtual teamsintermediate
- High degree of initiative and self-motivationintermediate
- Ability to manage multiple priorities successfullyintermediate
- Team-oriented, with a focus on achieving team goalsintermediate
- Ability to learn quickly, be organized and detail orientedintermediate
- Strong presentation and public speaking skillsintermediate
Required Qualifications
- Doctorate degree and 2 years of Computer Science, IT or related field experience (experience)
- Or Master’s degree and 4 years of Computer Science, IT or related field experience (experience)
- Or Bachelor’s degree and 6 years of Computer Science, IT or related field experience (experience)
- Or Associate’s degree and 10 years of Computer Science, IT or related field experience (experience)
- Or High school diploma / GED and 12 years of Computer Science, IT or related field experience (experience)
- Preferred: 3-5 years in AI/ML and enterprise software (experience)
- Knowledge graphs or structured reasoning (SPARQL, RDF/OWL) and tool-use planning with constraints (experience)
- Safety tooling (jailbreak detection, content moderation, PII redaction) and automated red-team pipelines (experience)
- Experience with privacy-preserving patterns (data masking, synthetic data, federated or on-prem inference) (experience)
- Domain familiarity in biopharma workflows (clinical operations, quality, manufacturing/supply chain, field/commercial) (experience)
- FinOps at scale: GPU fleet planning, utilization dashboards, commitment management (experience)
- Contributions to open source in LLM/agent ecosystems; patents or publications in conversational AI (experience)
- Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives (experience)
- Good-to-Have: Experience in Biotechnology or pharma industry (experience)
- Published thought-leadership or conference talks on enterprise GenAI adoption (experience)
- Master’s degree in Computer Science and or Data Science (experience)
- Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery (experience)
- Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) (experience)
Responsibilities
- Own the enterprise agent/bot platform architecture (cloud/on-prem), API contracts, and guardrails for multi-tenant use
- Design and ship production agentic systems: multi-agent planning, tool/function calling, workflow orchestration, and structured outputs
- Build high-containment bots for web/mobile/Slack/Teams/IVR with streaming responses and sub-second turn latency where required
- Implement retrieval & memory (RAG, vector stores, knowledge graphs, session memory) with data contracts, lineage, and lifecycle governance
- Establish bot CI/CD: prompt & config versioning, replay/conversation tests, feature flags, and automated rollbacks. Implement progressive delivery (blue-green, canary) with health checks, feature flags, and one-click rollback
- Stand up observability: tracing, metrics, and conversation analytics (containment, escalation, hallucination, CSAT, latency, cost per turn) with alerts and runbooks
- Integrate securely with enterprise systems (e.g., SAP, Salesforce, ServiceNow, MES/LIMS/ELN/QMS) via robust connectors and least-privilege tool access
- Embed safety, privacy, and compliance (content filtering, red-teaming, audit trails, secrets mgmt) aligned to GxP/21 CFR Part 11 and HIPAA/PII needs
- Optimize inference and cost (routing, caching, batching, distillation/quantization) and drive GPU/accelerator/FinOps strategy
- Evaluate and document trade-offs for models (open vs. proprietary), hosting options, and agent frameworks (e.g., LangChain, Semantic Kernel, Assistants APIs)
- Translate domain needs (R&D, Manufacturing, Commercial) into prioritized roadmaps; mentor teams and communicate technical trade-offs to stakeholders
Benefits
- general: Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
- general: Group medical, dental and vision coverage
- general: Life and disability insurance
- general: Flexible spending accounts
- general: Discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
- general: Stock-based long-term incentives
- general: Award-winning time-off plans
- general: Flexible work models, including remote and hybrid work arrangements, where possible
Tags & Categories