Below is a categorized, practical catalogue of AI-era job roles that either didn’t exist before or have been materially reshaped by AI adoption. Each role lists: title, purpose, responsibilities, skills/quals, where it’s hired, example employers, and why it’s growing.
AI Development & Engineering
1) Prompt Engineer / LLM Interaction Designer
Purpose: Design high-quality prompts, guardrails, and evaluation patterns to make models reliable for specific tasks.
Responsibilities: Prompt/RAG patterning; few-shot design; prompt testing & A/B; prompt libraries; eval dashboards.
Skills/Quals: Strong writing + reasoning; LLM APIs; vector search basics; prompt eval frameworks; portfolio > degree.
Industries: SaaS, search, productivity, marketing tech.
Examples: Microsoft/ Copilot, Google, Amazon, Meta; many startups. (Microsoft AI)
Outlook: Rapid growth as firms productionize LLM-based workflows; salaries competitive and remote-friendly. (PromptLayer)
2) Retrieval-Augmented Generation (RAG) Engineer
Purpose: Build apps that combine search/embeddings with LLMs for grounded answers.
Responsibilities: Corpus prep; embedding/ranking; chunking; context windows; evaluation; latency/quality tradeoffs.
Skills/Quals: Python/TypeScript; vector DBs (Pinecone, FAISS); evals; observability; API orchestration.
Industries: Enterprise SaaS, internal knowledge portals, customer support.
Examples: Many openings titled “RAG Engineer/LLM Engineer.” (Indeed)
Outlook: Exploding demand as RAG becomes the default enterprise pattern. (ZipRecruiter)
3) Vector Database Engineer
Purpose: Operate and optimize vector indexes for similarity search powering LLM apps.
Responsibilities: Schema & index design; ingestion pipelines; recall/precision tuning; ops & cost controls.
Skills/Quals: Distributed systems basics; Pinecone/Weaviate/pgvector; embeddings; monitoring.
Industries: Search, analytics, enterprise knowledge.
Examples: Pinecone ecosystem roles across markets. (Pinecone)
Outlook: Scale of enterprise retrieval keeps lifting demand.
4) AI Red Teamer / Adversarial ML Engineer
Purpose: Probe models for jailbreaks, prompt injection, data exfiltration, and safety gaps.
Responsibilities: Threat modeling; attack simulations; eval harnesses; incident playbooks; hardening feedback.
Skills/Quals: Security mindset; LLM safety/abuse taxonomies; scripting; prompt-attack patterns.
Industries: Foundation model labs, AI platforms, regulated sectors.
Examples: OpenAI/Anthropic post safety/security roles regularly. (OpenAI)
Outlook: Mandatory as AI gets deployed in sensitive workflows.
5) Applied Evals Engineer
Purpose: Build realistic, domain-specific evaluation suites that correlate with business outcomes.
Responsibilities: Scenario curation; task simulators; rubric design; offline/online evals; pipeline tooling.
Skills/Quals: Experiment design; metrics; Python; product sense.
Industries: B2B AI, productivity, customer ops.
Examples: OpenAI’s new “Applied Evals” team highlights this shift. (Business Insider)
Outlook: Booming as companies move past benchmark demos to measurable impact. (Business Insider)
6) AI Application Engineer (Agentic Systems)
Purpose: Ship agentic apps that call tools, browse, and act safely.
Responsibilities: Tool-use workflows; function calling; memory; guardrails; task decomposition; reliability engineering.
Skills/Quals: Python/JS; LLM tool-use; observability; product mindset.
Industries: Productivity, support, finance ops, devtools.
Examples: Microsoft/Research, Copilot platform roles. (Microsoft)
Outlook: Demand tracks the migration from chat to agents.
7) Synthetic Data Engineer
Purpose: Generate/privacy-preserve data to train/evaluate models where real data is scarce or sensitive.
Responsibilities: Scenario design; bias/utility checks; privacy guarantees; data evaluations.
Skills/Quals: Data pipelines; stats; privacy; synthetic tools.
Industries: Finance, health, automotive, retail.
Examples: Mostly AI, Gretel (now part of NVIDIA) and partners. (MOSTLY AI)
Outlook: Expands as enterprises de-risk data sharing/training.
8) Multimodal Engineer (Vision/Audio/Video)
Purpose: Build apps that mix text+vision+audio for search, analytics, and creation.
Responsibilities: OCR/transcription; image/video embedding; captioning; safety filters; latency tuning.
Skills/Quals: CV/audio basics; LLM APIs; pipelines; GPU savvy.
Industries: Retail, media, support QA, manufacturing.
Examples: Big tech and media-tech vendors. (Microsoft AI)
Outlook: New use-cases (meeting AI, creative tools) drive hires.
Data Management & Knowledge
9) AI Data Curator / Corpus Librarian
Purpose: Organize and maintain high-quality, rights-cleared corpora for training/RAG.
Responsibilities: Sourcing; licensing; dedupe; chunking; metadata; provenance; quality gates.
Skills/Quals: Data ops; IP basics; ETL; vectorization know-how.
Industries: Any enterprise adopting RAG/LLMs.
Examples: Listings inside content-heavy orgs and AI vendors. (Indeed)
Outlook: Core to model health; scales with every deployment.
10) AI Annotation Lead / RLHF Manager
Purpose: Run human-feedback and labeling programs for safer, aligned systems.
Responsibilities: Label specs; QA; inter-rater reliability; feedback loops to research.
Skills/Quals: Ops leadership; annotation platforms; ethics awareness.
Industries: Foundation model labs; data vendors; marketplaces.
Examples: Platforms like Scale, Appen; lab safety teams.
Outlook: Continues as models specialize by domain.
11) AI Knowledge Engineer (Enterprise)
Purpose: Map business knowledge to taxonomies/ontologies and RAG structures.
Responsibilities: Source-of-truth design; doc lifecycles; embeddings strategy; governance.
Skills/Quals: Info architecture; search; vector DBs.
Industries: Professional services, support, regulated industries.
Examples: RAG roles across enterprise job boards. (Indeed)
Outlook: Knowledge sprawl + RAG adoption = sustained hiring.
Ethical, Safety & Regulatory
12) AI Governance Lead / AI Policy Manager
Purpose: Define policies for responsible AI, audits, model risk, and compliance.
Responsibilities: Policy frameworks; model inventories; approvals; third-party risk; reporting.
Skills/Quals: Policy/compliance; risk; stakeholder mgmt.
Industries: Tech, finance, healthcare, public sector.
Examples: Hundreds of AI governance postings in tech hubs. (Indeed)
Outlook: Regulation + board oversight push headcount.
13) Model Risk Manager (AI)
Purpose: Apply model-risk practices (banks/insurance) to LLMs/ML systems.
Responsibilities: Validation, stress tests, documentation, change control.
Skills/Quals: Quant/risk literacy; audit communication.
Industries: Financial services, gov, healthcare.
Examples: Large banks/insurers expanding AI risk teams.
Outlook: Required under evolving AI/AML/operational risk regimes.
14) AI Safety Researcher / Alignment Engineer
Purpose: Investigate robustness, misuse, and alignment issues.
Responsibilities: Red-teaming; evals; interpretability; mitigations.
Skills/Quals: Research mindset; scripting; safety literature.
Industries: Foundation model labs, safety-led startups.
Examples: Anthropic/OpenAI safety careers. (Anthropic)
Outlook: Expands with model capabilities and policy pressure.
AI Product & Service Delivery
15) AI Product Manager (LLM/Copilot)
Purpose: Own AI features, from problem discovery to metrics/guardrails.
Responsibilities: PRDs; eval metrics; human-in-the-loop design; privacy; launch & adoption.
Skills/Quals: PM toolkit; LLM patterns; UX; analytics.
Industries: Horizontal productivity, vertical SaaS.
Examples: Copilot/enterprise AI PM roles. (Microsoft)
Outlook: Every software category is adding “AI mode.”
16) Conversational UX Designer / Voice UX
Purpose: Design chat/voice flows that feel natural and safe.
Responsibilities: Conversation schemas; fallback/repair; persona; tone; analytics loops.
Skills/Quals: Service design; linguistics; prototyping; NLU basics.
Industries: Support, healthcare, travel, retail.
Examples: Broader Copilot/assistant ecosystem hiring. (Microsoft AI)
Outlook: Surges as voice & chat become default interfaces.
17) AI Solutions Architect
Purpose: Translate business use-cases into scalable AI architectures.
Responsibilities: Tool/stack selection; RAG/agents; cost-latency tradeoffs; security reviews.
Skills/Quals: Cloud; data; LLMOps; stakeholder alignment.
Industries: SI/consulting, enterprise IT, SaaS.
Examples: Accenture-style integrators scaling AI services. (Business Insider)
Outlook: Enterprises need guides from prototype to production.
AI Operations, Reliability & Security
18) LLMOps / MLOps Engineer (GenAI)
Purpose: Deploy/monitor LLM apps at scale.
Responsibilities: CI/CD; offline/online evals; tracing; drift & jailbreak detection; cost controls.
Skills/Quals: Cloud; feature flags; eval tooling; logging.
Industries: Any GenAI product team.
Examples: Roles tied to Copilot & enterprise AI stacks. (Microsoft)
Outlook: Essential as usage grows and SLAs tighten.
19) AI Observability Engineer
Purpose: Build telemetry for prompts, contexts, outputs, and user feedback.
Responsibilities: Metrics; alerts; safety/quality dashboards; data flywheels.
Skills/Quals: Data eng; tracing; eval infra.
Industries: SaaS, support, analytics vendors.
Examples: Vendors and large enterprise AI teams.
Outlook: Needed for compliance and reliability at scale.
20) AI Security Engineer (LLM Security)
Purpose: Secure model endpoints, context stores, and agent toolchains.
Responsibilities: Threat modeling; supply-chain checks; prompt-injection defenses; data leakage prevention.
Skills/Quals: AppSec + LLM threat patterns; testing.
Industries: All regulated or IP-sensitive environments.
Examples: Platform/lab roles; security startups.
Outlook: Attack surface expands with agents and RAG.
AI Consulting & Strategy
21) GenAI Transformation Consultant
Purpose: Identify high-ROI use-cases; create adoption roadmaps.
Responsibilities: Process discovery; ROI modeling; vendor selection; pilot runbooks; change mgmt.
Skills/Quals: Consulting toolkit; domain knowledge; LLM literacy.
Industries: Cross-industry; mid-market to enterprise.
Examples: Accenture and boutique firms actively hiring. (Business Insider)
Outlook: Every function is evaluating “Copilot for X.”
22) AI Readiness Assessor / Auditor
Purpose: Evaluate data, security, and workflows for AI suitability.
Responsibilities: Current-state assessments; risk registers; roadmap scoring; governance handoff.
Skills/Quals: Data maturity models; security/privacy; reporting.
Industries: Enterprise/regulated sectors.
Examples: Consulting and internal AI centers of excellence.
Outlook: Compliance + risk oversight fuel demand.
AI Education & Training
23) Corporate AI Literacy Trainer
Purpose: Upskill non-technical teams to use AI safely and effectively.
Responsibilities: Curriculum; hands-on workshops; playbooks; role-specific templates.
Skills/Quals: Teaching; LLM tools; facilitation.
Industries: All—sales, marketing, HR, ops.
Examples: Many firms rolling out company-wide training programs. (Business Insider)
Outlook: Upskilling is a board-level priority. (Business Insider)
24) AI Prompting Coach / Writing Coach
Purpose: Coach teams on prompt patterns for their workflows.
Responsibilities: Office hours; libraries; prompt QA; measurement.
Skills/Quals: Communication; LLM proficiency; documentation.
Industries: Agencies, content teams, CX.
Examples: Startups, agencies, in-house enablement teams.
Outlook: Low barrier + high leverage → steady demand.
AI-Enhanced Creative & Media
25) Generative Video Producer
Purpose: Produce explainers/ads using AI video, TTS, and stock.
Responsibilities: Scripting; storyboards; model runs; editing; compliance checks.
Skills/Quals: Creative direction; video tools; gen-video platforms.
Industries: Marketing, education, media.
Examples: Agencies and creator-economy studios.
Outlook: Short-form content explosion.
26) Conversational Designer (Marketing)
Purpose: Build chat journeys for lead gen and support.
Responsibilities: Flows; tone; A/B tests; guardrails; analytics.
Skills/Quals: UX writing; bot platforms; LLMs.
Industries: Ecommerce, SaaS, travel.
Examples: Messenger/IG/website bot teams.
Outlook: Chat funnels are now baseline.
27) AI Audio Producer / Voice Designer
Purpose: Create brand voices, narration, and dialogue with TTS and cloning.
Responsibilities: Script polish; voice selection; licensing; post-production.
Skills/Quals: Audio editing; TTS; IP hygiene.
Industries: Ads, games, learning.
Examples: Adtech/edtech studios.
Outlook: Cheaper audio creation lifts volume.
AI-Driven Business Functions
28) Sales Copilot Admin / RevOps AI Specialist
Purpose: Operationalize AI in CRM, sequences, and forecasting.
Responsibilities: Cadence templates; assistive email/pitch assets; AI notes; pipeline QA.
Skills/Quals: CRM tools; prompt ops; analytics.
Industries: B2B/B2C sales orgs.
Examples: Enterprises rolling out Copilots. (Microsoft AI)
Outlook: Direct revenue impact → resilient hiring.
29) Support Copilot Designer
Purpose: Blend knowledge + LLMs to boost agent deflection and CSAT.
Responsibilities: RAG KBs; macro authoring; safe escalation; measurement.
Skills/Quals: Helpdesk suites; LLMs; writing.
Industries: SaaS, ecommerce, telco.
Examples: Internal support excellence teams.
Outlook: Efficient support is a top GenAI use-case.
30) HR/People Ops AI Specialist
Purpose: Apply AI to sourcing, screening, onboarding, and talent insights.
Responsibilities: GPT-assisted JD writing; search & outreach; interview guides; policy checks.
Skills/Quals: HR tech; data hygiene; bias mitigation.
Industries: All mid-large firms.
Examples: Recruiting sourcer roles trend upward. (Business Insider)
Outlook: Productivity + talent shortage sustain demand.
Domain-Specific “AI for X” Roles
31) Legal AI Operations (Non-attorney)
Purpose: Operate doc review/summarization, clause extraction, and RAG for legal teams.
Responsibilities: Corpus curation; prompt/eval; workflow QA; audit trails.
Skills/Quals: Legal ops familiarity; LLMs; governance.
Industries: Legal ops, ALSPs, in-house.
Examples: Growing in law-tech vendors and enterprises.
Outlook: Volume & speed pressures drive adoption.
32) Healthcare AI Navigator (Non-clinical)
Purpose: Deploy AI scribes, prior-auth assistants, and patient support bots.
Responsibilities: Workflow mapping; PHI controls; evals; training & rollouts.
Skills/Quals: Health data privacy; LLMs; change mgmt.
Industries: Providers, payers, healthtech.
Examples: Hospital IT/ops teams partnering with vendors.
Outlook: Workforce gaps + admin burden → sustained growth.
33) Financial Research Copilot Specialist
Purpose: Build/evaluate AI research tools for analysts, FP&A, and ops.
Responsibilities: Data connectors; policy rails; documentation; audits.
Skills/Quals: Finance domain + LLMs; spreadsheet fluency.
Industries: Banks, PE/VC, fintech, corp finance.
Examples: Model-risk & AI ops hiring in finance.
Outlook: Faster analysis with controls = budget priority.
Ecosystem & Platform Roles
34) AI Partner Engineer / Ecosystem Developer
Purpose: Help customers/partners integrate platform APIs and best practices.
Responsibilities: Solution demos; sample apps; field feedback to product.
Skills/Quals: Developer relations + LLM stack; teaching.
Industries: Vector DBs, model APIs, tooling vendors.
Examples: Pinecone, foundation-model and Copilot ecosystems. (Pinecone)
Outlook: Platforms compete via ecosystem depth.
35) Community Strategist (AI Builders)
Purpose: Grow developer/creator communities around AI tools.
Responsibilities: Content; meetups; templates; showcase programs.
Skills/Quals: Evangelism; content; hands-on demos.
Industries: AI devtools, design tools, agents.
Examples: Model/tool vendors hiring community roles.
Outlook: Differentiation shifts to enablement.
Operations, Change & Talent
36) AI Change Manager
Purpose: Drive adoption—process redesign, training, comms, and metrics.
Responsibilities: Stakeholder maps; enablement; KPI tracking; feedback loops.
Skills/Quals: Change frameworks; facilitation; LLM literacy.
Industries: Every enterprise deploying AI.
Examples: SI/consultancies and in-house programs. (Business Insider)
Outlook: Scale-up phase favors specialists.
37) AI Talent Sourcer (Specialist)
Purpose: Find RAG/prompt/LLMOps/security talent globally.
Responsibilities: Sourcing, JD refinement, assessments, pipelines.
Skills/Quals: Recruiter toolkit; AI skill taxonomies.
Industries: Tech, consulting, enterprise COEs.
Examples: Expanding hiring velocity across labs/enterprises. (The Times of India)
Outlook: Demand outruns supply for niche skills.
Public Sector & Policy
38) AI Policy Advisor / Public-Interest Technologist
Purpose: Shape AI policy, standards, and public-sector deployments.
Responsibilities: Draft guidance; stakeholder engagement; risk/impact analysis.
Skills/Quals: Policy analysis; tech literacy; consensus-building.
Industries: Government, NGOs, multilaterals.
Examples: Municipal/state/federal roles labeled AI policy/governance. (Indeed)
Outlook: New laws and standards → durable hiring.
Research & Advanced Roles
39) Applied Research Engineer (GenAI)
Purpose: Bridge research to product in targeted domains.
Responsibilities: Prototype; evaluate; productionize; knowledge transfer.
Skills/Quals: Strong coding; experiment design; domain depth.
Industries: Labs and product orgs.
Examples: OpenAI/Anthropic & big-tech research openings. (OpenAI)
Outlook: “Last mile” from paper to product is the bottleneck.
40) Program Manager, AI Expansion / Internationalization
Purpose: Scale AI programs across markets, languages, and partners.
Responsibilities: Localization; partnerships; enablement; compliance checks.
Skills/Quals: Ops leadership; cross-border collaboration; LLM basics.
Industries: Foundation model companies, global SaaS.
Examples: Anthropic expanding internationally. (The Times of India)
Outlook: Global growth and localization sustain demand.
Supportive & Hybrid New Roles
41) AI Content QA Editor
Purpose: Human-in-the-loop quality control for AI copy, visuals, and data outputs.
Responsibilities: Fact-check; tone/style QA; bias checks; redlines; escalation.
Skills/Quals: Editing; research; AI literacy; domain basics.
Industries: Media, marketing, enterprise comms.
Examples: Agencies and in-house content ops.
Outlook: Trust requires human editorial layers.
42) AI Accessibility & Inclusive Design Specialist
Purpose: Ensure AI interfaces/content meet accessibility and inclusion standards.
Responsibilities: Testing; alt modalities; policy alignment; feedback loops.
Skills/Quals: Accessibility standards; UX; assistive tech.
Industries: Public sector, education, enterprise UX.
Examples: Larger orgs and vendors with compliance needs.
Outlook: Policy + brand risk → rising relevance.
43) AI Carbon/Sustainability Analyst
Purpose: Measure and reduce compute footprint and supply-chain impacts.
Responsibilities: Emissions models; workload planning; vendor benchmarking; reporting.
Skills/Quals: Carbon accounting; cloud metering; scenario analysis.
Industries: Any AI-at-scale org; cloud users.
Examples: Enterprises with ESG mandates.
Outlook: Compute growth + ESG reporting drive hiring.
Why these roles are expanding (macro signals)
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From demos to production: Firms want applied evaluations tied to KPIs, not just benchmark scores. (Business Insider)
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Globalization of model providers: Labs are staffing in new countries/functions (policy, partner engineering, applied AI). (The Times of India)
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Enterprise transformation: Large integrators are retraining and hiring into GenAI practices at scale. (Business Insider)
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Skill specialization: Job boards show surging postings for RAG/vector DB/governance/Copilot roles. (Indeed)
Quick index of additional titles you’ll see in postings
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Development/Engineering: LLM Engineer; Agent Engineer; Multimodal Engineer; Model Tooling Engineer; AI Evals Engineer; GenAI Backend Engineer; Prompt Library Maintainer.
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Data/Knowledge: Corpus Manager; Data Provenance Lead; Ground-Truth Program Manager; Synthetic Data PM.
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Ethics/Safety/Reg: AI Governance Lead; AI Risk & Controls Manager; Safety Ops Specialist; Red-Team Operator; AI Incident Response Lead.
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Product/Delivery: AI PM (Copilot); Conversational UX; AI Solutions Architect; AI Partner Engineer; Field AI Engineer.
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Ops/Sec: LLMOps Engineer; AI Observability Engineer; AI Security/LLM AppSec; Vector DB SRE.
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Consulting/Enablement: GenAI Strategy Consultant; AI Readiness Assessor; Corporate AI Trainer; Prompting Coach.
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Creative/Media: Generative Video Producer; AI Audio Producer; Content QA Editor; AI Localization Producer.
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Business Functions: Sales Copilot Admin; Support Copilot Designer; HR AI Specialist; Finance Copilot Analyst.
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Public Sector/Policy: AI Policy Advisor; Responsible AI Program Manager; Standards & Testing Analyst.
Notes & caveats
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Titles vary widely by company; scan descriptions for keywords like RAG, vector database, Copilot, prompt engineering, AI governance, AI safety, LLMOps. Job boards and vendor career pages (e.g., Microsoft AI, Pinecone, OpenAI/Anthropic, Indeed/ZipRecruiter) are reliable places to see demand trends and concrete postings. (Microsoft AI)
