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Retrieval-augmented generation jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-09-30, Retrieval-augmented generation appears in 579 job postings indexed by Skillenai over the past 90 days — AI Engineer has the most postings mentioning Retrieval-augmented generation, with demand share up 16.4% vs the prior 4 weeks.

Last updated · 90d ending 2026-09-30

Postings · last 90 days
579
Demand vs prior month
up 16.4% vs the prior 4 weeks
Top role · 11.9% of skill postings
AI Engineer
Top hiring metro
New York City

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Frequently asked questions about Retrieval-augmented generation

+Is Retrieval-augmented generation in demand in 2026?

Yes. Retrieval-augmented generation appears in 579 job postings indexed by Skillenai over the 90 days ending 2026-09-30. AI Engineer accounts for the most postings mentioning Retrieval-augmented generation (11.9% of all postings mentioning Retrieval-augmented generation).

+What jobs require Retrieval-augmented generation?

According to the Skillenai jobs index over the 90 days ending 2026-09-30, among roles with at least 20 postings, the highest shares mentioning Retrieval-augmented generation are Platform Product Manager (10.0% of that role’s postings mention Retrieval-augmented generation), Customer Solutions Architect (9.1% of that role’s postings mention Retrieval-augmented generation), Applied AI Scientist (6.9% of that role’s postings mention Retrieval-augmented generation).

+What skills are commonly paired with Retrieval-augmented generation?

Across job postings indexed by Skillenai (90 days ending 2026-09-30), Retrieval-augmented generation most often appears alongside Python, prompt engineering, machine learning, observability, large language models.

+Where is Retrieval-augmented generation most in demand?

As of 2026-09-30, the metro areas posting the most jobs requiring Retrieval-augmented generation are New York City, London, San Francisco, Toronto, Singapore, according to the Skillenai jobs index.

+How can I keep up with new Retrieval-augmented generation content and jobs?

Skillenai indexes news, blog posts, and research papers mentioning Retrieval-augmented generation alongside the jobs index. You can subscribe to a daily email digest of new Retrieval-augmented generation content from your Skillenai account.

+Which skills come before and after Retrieval-augmented generation?

The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.

Weekly indexed postings requiring Retrieval-augmented generation — last 90 days

Salary distribution

Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized

Career paths around Retrieval-augmented generation

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before Retrieval-augmented generation

Before Retrieval-augmented generationpython → Retrieval-augmented generation: 4 observed employer moves with this skill pairscikit-learn → Retrieval-augmented generation: 2 observed employer moves with this skill pairAgile → Retrieval-augmented generation: 2 observed employer moves with this skill pairaws sagemaker → Retrieval-augmented generation: 2 observed employer moves with this skill pairBERT → Retrieval-augmented generation: 2 observed employer moves with this skill pairTransformer models → Retrieval-augmented generation: 2 observed employer moves with this skill pairxgboost → Retrieval-augmented generation: 2 observed employer moves with this skill pairJavaScript → Retrieval-augmented generation: 2 observed employer moves with this skill pairRetrieval-au…generationpython: 4 movespython4 movesscikit-learn: 2 movesscikit-learn2 movesAgile: 2 movesAgile2 movesaws sagemaker: 2 movesaws sagemaker2 movesBERT: 2 movesBERT2 movesTransformer models: 2 movesTransformer models2 movesxgboost: 2 movesxgboost2 movesJavaScript: 2 movesJavaScript2 moves

Skills after Retrieval-augmented generation

After Retrieval-augmented generationRetrieval-augmented generation → LLM APIs: 1 observed employer moves with this skill pairRetrieval-augmented generation → scikit-learn: 1 observed employer moves with this skill pairRetrieval-augmented generation → ETL: 1 observed employer moves with this skill pairRetrieval-augmented generation → Grok: 1 observed employer moves with this skill pairRetrieval-augmented generation → Python Scripts: 1 observed employer moves with this skill pairRetrieval-augmented generation → collaboration environment: 1 observed employer moves with this skill pairRetrieval-augmented generation → Salesforce: 1 observed employer moves with this skill pairRetrieval-augmented generation → speed tier: 1 observed employer moves with this skill pairRetrieval-au…generationLLM APIs: 1 movesLLM APIs1 movesscikit-learn: 1 movesscikit-learn1 movesETL: 1 movesETL1 movesGrok: 1 movesGrok1 movesPython Scripts: 1 movesPython Scripts1 movescollaboration environment: 1 movescollaborationenvironment1 movesSalesforce: 1 movesSalesforce1 movesspeed tier: 1 movesspeed tier1 moves
How to read this chart · view counts

Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.

The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.

Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.

Observed connections and move counts
ConnectionMoves
Before: python4
Before: scikit-learn2
Before: Agile2
Before: aws sagemaker2
Before: BERT2
Before: Transformer models2
Before: xgboost2
Before: JavaScript2
After: LLM APIs1
After: scikit-learn1
After: ETL1
After: Grok1
After: Python Scripts1
After: collaboration environment1
After: Salesforce1
After: speed tier1

Roles most likely to require Retrieval-augmented generation

Among roles with at least 20 postings in the same period.

RolePostings mentioning skill% of role postings mentioning skill
Platform Product Manager210.0%
Customer Solutions Architect29.1%
Applied AI Scientist26.9%
Data & AI Engineer26.9%
Machine Learning Research Engineer25.9%
AI Data Engineer45.6%
Security Engineering Manager35.2%
Deployed Engineer14.8%
Developer Experience Engineer14.8%
Postdoctoral Researcher14.8%

Roles with the most Retrieval-augmented generation postings

RolePostings mentioning skillShare of skill postings
AI Engineer6911.9%
Software Engineer579.8%
Data Scientist305.2%
Machine Learning Engineer264.5%
Forward Deployed Engineer183.1%
Product Manager183.1%
Applied AI Engineer132.2%
Full Stack Engineer132.2%
ML Engineer111.9%
AI/ML Engineer81.4%

Top companies posting jobs requiring Retrieval-augmented generation

Employers ranked by indexed job postings in the last 90 days.

Top companies posting jobs requiring Retrieval-augmented generation
CompanyPostings · 90 days
Adobe12
Elsevier9
NVIDIA9
Bosch8
JJ8
Capital One7
Cisco7
Novartis6
eBay6
Barclays6

Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.

Top metros hiring for Retrieval-augmented generation

NamePostingsShare
New York City254.3%
London203.5%
San Francisco193.3%
Toronto172.9%
Singapore122.1%
Boston91.6%
San Jose91.6%
Hyderabad81.4%
Washington81.4%

Skills commonly paired with Retrieval-augmented generation

Python (325)prompt engineering (159)machine learning (139)observability (118)large language models (104)APIs (103)Generative AI (99)embeddings (97)SQL (94)model evaluation (93)

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How this was computed

Counts derive from the Skillenai jobs index over the 90 days ending 2026-09-30. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning Retrieval-augmented generation by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Retrieval-augmented generation postings by all Retrieval-augmented generation postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). Adjusted posting share: 0.2% to 0.2%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,596 identified employers and covers 68% of earlier and 72% of latest indexed postings. Windows: 2026-06-02 to 2026-08-31 and 2026-06-30 to 2026-09-28 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.

source
Skillenai jobs index, deduplicated daily
entity_id
bbb073fbef2a4135
data_as_of
2026-09-30
window_days
90
Hiring engineers who use Retrieval-augmented generation?

The demand, skills, and geo numbers on this page come from the same Skillenai labor market index that powers our API. Use it for compensation benchmarking, hiring-competition analysis, and skill-adoption tracking.

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Compiled by Jared Rand · Data sourced from the Skillenai labor market index