GenAI and Agentic Systems

Market-leading GenAI Recruitment. Connect with the Best Opportunities in Deep Tech.

Whether you’re fine-tuning foundation models, building RAG pipelines, or developing agentic systems that blend text, audio, and vision, GenAI careers are redefining what it means to solve complex challenges at scale.

In the world of Deep Tech recruitment, defensible, dependable talent pipelines are the difference between scaling breakthrough technologies and stalling out before they ship.

Building something bold? We’ll find you the people to do it with. Connect with our GenAI specialists:

Talk to a GenAI consultant

Looking for the best jobs in GenAI? We have the opportunities:

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Why Choose DeepRec.ai for GenAI Recruitment? 

  • Our GenAI recruitment practice operates through dedicated delivery teams focused exclusively on this space. Within that, we maintain specialist coverage across core GenAI skill sets - including agentic architectures and Natural Language Processing (NLP) - enabling us to build deep market insight, long-term candidate relationships, and execute with speed in highly competitive talent markets.

  • We combine a global reach with a granular understanding of local market trends. Our Deep Tech headhunters work with leading businesses across the UK, Ireland, Switzerland, Germany, and the US.
  • DeepRec.ai's community-led approach to recruitment creates access to hard-to-reach talent. In talent-scarce markets (like most of deep tech), the best candidates are often passive and difficult to access through traditional means. We built a global deep tech community to connect exceptional talent with rewarding opportunities, creating value for both candidates and the organisations building at the bleeding edge. 

  • We are SECO and AUG-licensed, enabling us to provide compliant, high-quality cross-border talent solutions across Switzerland & Germany.

  • DeepRec.ai sits alongside our sister brands, Broadgate, Trust in SODA, and Sorai, to complete Trinnovo Group's ecosystem. This provides us with access to unparalleled market expertise under one roof. Operating at the intersection of technology, regulation, and advanced technology, we are uniquely positioned to support the most pressing and complex needs of modern business. 

  • As part of Trinnovo Group, DeepRec.ai is B Corp certified, reflecting our commitment to operating responsibly, ethically, and with long-term impact across our clients, candidates, and the wider tech ecosystem.

Where is GenAI Recruitment Heading? 

When it comes to GenAI, the pace of change is relentless, and the opportunities are endless. New roles are emerging faster than companies can adapt, and top talent is harder to find (and retain).

We know that speed, precision, and agility are essential when the stakes are high and the market's moving quickly. Every hiring project is unique in GenAI, and your recruitment solution needs to be just as dynamic. 

Contract, Permanent, retained, embedded, executive recruitment – DeepRec.ai have the means to match the best candidates with the brightest opportunities in GenAI. 

How Competitive is the Talent Market Right Now? 

There's a limited talent pool to source from, and traditional hiring methods won't cut it, especially when employers are forced to compete against massive budgets. At a glance: 

  • GenAI roles have seen immense growth in financial services and the life sciences. Deep Tech startups and scaleups are now forced to compete with a much broader range of industries. 

  • Companies are paying a high premium on skill sets with the GenAI tag. Expect a 5% minimum salary uplift

  • Speed is a key differentiator. While this is always the case in recruitment, Deep Tech, and GenAI in particular, move faster than most markets. Top candidates will not wait around for a drawn-out process; there are too many opportunities out there. 

  • Currently, experience is highly concentrated. This means that the majority of real-world GenAI deployment experience sits with a small group of candidates. 

  • Talent mobility is a major consideration for today's employers. With limited talent pools, relocation capabilities are essential. We've supported various companies with this in recent hiring mandates, and it's helped them retain precision and speed in the process.

The Rise of NLP

The rise of Natural Language Processing (NLP) has taken the world by storm, and we’re here for it. NLP has played a defining role in the evolution of generative AI, ranging from predictive text all the way through to fully-fledged virtual companions. Demand for talent and investment is sky high. 

As GenAI adoption accelerates, NLP capability has shifted from experimentation to production. This latest phase is driving strong demand for domain-specific expertise, where talent pools are short, projects are time-sensitive, and competition for experienced practitioners is intense. 

Increasingly, NLP capability is being embedded within agentic GenAI systems, where language models act as decision-making components inside larger autonomous workflows

This level of demand requires more than generalist recruitment support. Hiring effectively in NLP-driven GenAI environments depends on a deep understanding of evolving role definitions, technical requirements, and highly competitive talent markets.

Through sustained engagement with our dedicated AI community, a wealth of experience earned from successful hiring mandates, and a deep technical fluency, DeepRec.ai's consultants have a clear, real-time view of how NLP roles differ across domains, industries, and stages of growth.

Looking for a precise and dependable NLP talent partner?

Connect with a Consultant

 

GENAI AND AGENTIC SYSTEMS CONSULTANTS

Anthony Kelly

Co-Founder & MD EU/UK

Hayley Killengrey

Co-Founder & MD USA

Jonathan Harrold

Consultant - Germany

Benjamin Reavill

Consultant - US

LATEST JOBS

Remote work, England
VP of Agentic Systems
VP of Agentic SystemsRemote Across Europe We are building a next-generation AI platform powering agent-driven systems and advanced digital creation tools. We are seeking a VP of Agentic Engineering to lead the architecture, scaling, and operational excellence of our core AI platform. This is not a research role. It is a production-scale engineering leadership position focused on building secure, scalable, enterprise-grade systems. What You’ll OwnDefine and lead the Architectural Platform that underpins all AI capabilitiesBuild and scale a wider enterprise platform that ingests, stores, processes, and exposes large-scale data into downstream creation toolsDesign multi-tenant, cloud-native core services and APIsProductionize GenAI systems (LLMs, agent-based systems) into reliable platform servicesLead AI orchestration, model serving, and high-throughput data infrastructureEnsure enterprise-grade reliability, security, performance, and observabilityScale and lead high-performing platform and AI engineering teamsMust HaveProven experience building and scaling enterprise web platformsHands-on experience productionizing Generative AI systems, including LLMs and AI agentsDeep expertise in distributed systems and cloud infrastructure (AWS, GCP, or Azure)Experience designing enterprise-scale data ingestion and storage platformsExperience leading teams of 20 engineers
Anthony KellyAnthony Kelly
Zürich, Switzerland
Multimodal AI Systems: Principal Technical Leader/ Chief Scientist
Multimodal AI Systems: Chief Scientist/ Principal Technical Leader We are seeking a senior leader to define and deliver the architecture and research direction for large-scale multimodal AI systems. This role combines scientific leadership with hands-on system ownership, spanning model innovation, training, inference, and production deployment. You will lead the design of multimodal architectures across LLMs, VLMs, video models, and multimodal agents, while driving cutting-edge research in multimodal understanding and generation. The role owns the full lifecycle from novel algorithms and publications to scalable, optimized systems (autotuning, quantization, inference efficiency). RequirementsDeep expertise in multimodal learning with hands-on experience training large-scale vision-language, video, or multimodal models.Strong understanding of transformers, diffusion models, and large multimodal model inference.Proven research impact (top-tier conferences preferred) and/or significant open-source contributions.Ability to translate frontier research into production-grade AI systems.
Anthony KellyAnthony Kelly
San Francisco, California, United States
LLM Algorithm Tech Lead
LLM Algorithm Lead$200,000 - $300,000San Francisco, HybridFull-time / PermanentA product-focused AI start-up is building LLM systems that run in production and are used daily by over a million professionals. This role is responsible for designing, shipping, and maintaining applied LLM systems that support real product features, with an emphasis on reliability, cost, and scale rather than experimentation. Why This Role MattersOwn how LLM systems behave in a large, user-facing productMake architectural decisions that affect reliability, latency, and costMove LLM features from prototype to stable production systemsSet technical direction for applied LLM algorithms and evaluation practicesWhat You’ll DoDesign structured LLM workflows, including planning, reasoning, and multi-step executionBuild and maintain core components such as memory, personalization, and reusable LLM modulesLead development of LLM-powered product features from design through productionBuild and optimize retrieval pipelines (RAG) via chunking, indexing, reranking, and evaluationSelect and route between models based on performance, cost, and latency constraintsDefine evaluation metrics, monitoring, and feedback loopsDebug production issues and drive algorithm-level improvementsWhat You BringExperience shipping LLM-based systems into productionStrong understanding of prompting, reasoning workflows, and system designHands-on experience with RAG systemsExperience building evaluation, monitoring, or safety mechanismsAbility to lead technical decisions and guide other engineersExperience with inference optimization, efficiency, or large-scale systems is a plus
Benjamin ReavillBenjamin Reavill
San Francisco, California, United States
Applied AI Engineer
AI Applied Engineer$200,000 - $300,000San Francisco, HybridPermanent / Full-timeA product-led AI start-up is building one of the most widely adopted AI work companions in the world, operating at massive real-user scale with millions of professionals relying on it daily. The challenge problem now is designing AI systems that reliably support complex knowledge work across preparation, collaboration, and follow-through, inside products people trust. This role is ideal for someone who wants to work across AI engineering, product thinking, and ultimately shape how AI actually shows up in day-to-day professional workflows. Why This Role MattersOwn how AI supports high-stakes knowledge workDesign multi-step AI workflows that users rely on repeatedlyHelp define how agent-like systems behave inside a consumer-grade productWork beyond prompt design into evaluation, iteration, and reliabilityWhat You’ll DoOwn the end-to-end design of AI-first workflows for preparation, collaboration, and follow-up Design and iterate multi-step LLM / agentic systems, spanning intent understanding, planning, tool invocation, memory usage, and refinement loopsBuild reusable AI skills, prompts, templates, and evaluation pipelines that can power multiple product experiencesDefine success metrics for AI behaviour, run experiments, and use real interaction data to improve usefulness and reliabilityPartner closely with engineering and ML teams to ship quickly while maintaining a high bar for product quality and user experienceWhat You BringProven experience shipping AI/ML powered products end to endStrong working understanding of LLM systems: prompting, tool calling, retrieval, context construction, evaluation, and common failure modesAbility to translate user needs into clear flows, specs, and examples, including edge cases and expected behavioursComfort working directly with data and interaction logs to debug issues and compare variantsHands-on experience designing agent-like workflows involving multi-step plans, multiple tools, and refinement or self-correction
Benjamin ReavillBenjamin Reavill
San Francisco, California, United States
Agentic AI Engineer
Agentic AI Engineer$200,000 - $300,000San Francisco, HybridPermanent / Full-timeA product-led AI start-up is building one of the most widely adopted AI work companions in the world, operating at massive real-user scale with millions of daily interactions. The challenge has shifted to designing agent systems that can plan, reason, evaluate themselves, and operate reliably inside real products. This is an opportunity to work from first principles on agentic architectures that power production systems used by professionals globally. Why This Role MattersBuild agent systems that plan, act, reflect, and improve across complex, ambiguous user workflowsDefine foundational patterns for LLM tool-use, reasoning graphs, and self-evaluation in productionJoin at a point where agent architecture decisions will shape the long-term platformWork on problems beyond prompt engineering like runtime reliability, context limits, and learning flywheelsWhat You’ll DoDesign and implement Plan–Act–Reflection style agent architecturesBuild DAG-based reasoning flows to deconstruct user intent into executable stepsDevelop agent skills including function calling, MCP-style integrations, and streaming APIsSolve runtime problems like context overflow / context rot through isolation, compression, and offloading strategiesArchitect automated evaluation and learning pipelines (reward functions, LLM-as-judge, RFT-style systems)What You BringProven experience building and shipping agentic AI systemsStrong understanding of workflow design, failure modes, and deterministic executionComfort designing distributed systems, APIs, and protocols used across teamsPractical experience with agent orchestration frameworks
Benjamin ReavillBenjamin Reavill
Zug, Switzerland
Applied AI Engineer - Zurich
Machine Learning EngineerLocation: Zurich / RemoteLanguage: German/Swiss German Preferred (English Fluent/Professional) This is a priority search for a small investment group building an internal AI Lab across companies they actively own.The company operates across retail and FMCG supply chains. Their portfolio supports large offline and online retailers, with heavy operational workflows across trading, pricing, ERP, and CRM. Today a lot of value is lost to manual processes and fragmented systems.This hire will be the first dedicated technical builder in the AI Lab. The focus is hands on delivery, not research.The expectation is to design, build, and ship AI driven systems that improve trader productivity, reduce operational friction, and surface revenue opportunities. This includes agent based workflows, internal tools, and hands on AI and ML implementation.You would work closely with founders and operators, move quickly, and have real autonomy. There is also exposure to reviewing the technical architecture of new investments and shaping build decisions early.Small, elite team distributed across Europe. High responsibility and clear ownership from day one. Real upside tied to equity backed projects.Ideally Switzerland based. German or Swiss German is a strong plus, English is also required.If you enjoy building in ambiguous environments and want your work in production immediately, feel free to send your CV.
Nathan WillsNathan Wills
California, United States
Senior Applied AI Engineer
Applied AI Engineer (End-to-End ML)Location: Palo Alto, CA (Hybrid)Role Type: Full-Time / PermanentOur client, a pioneering HealthTech AI company in Palo Alto, is seeking a high-calibre Applied AI Engineer to bridge the gap between advanced Machine Learning and robust Software Engineering. This is an end-to-end ownership role: you will be responsible for designing the logic, building the architecture, and deploying the final services. Core ResponsibilitiesArchitect AI Workflows: Design and implement sophisticated agentic workflows and automation sequences that power clinical decision-making.System Design & Integration: Build the backend infrastructure, scalable REST APIs, and data services required to support high-concurrency AI applications.Rapid Deployment: Maintain a high-velocity shipping cycle, moving from prototype to production-grade implementation in days.Model Orchestration: Select, fine-tune, and evaluate the performance of various LLMs (including OpenAI, Anthropic, and open-source models) for specific healthcare tasks.Full-Stack ML: Own the pipeline from data ingestion and time-series forecasting to real-time classification and model monitoring.Technical ProfileComputer Science Mastery: Expert knowledge of algorithms, data structures, and distributed systems.Software-Heavy Background: Professional-grade Python skills. You should be comfortable with software design patterns, testing, and CI/CD.Machine Learning Fundamentals: * Deep understanding of Core ML topics: classification, regression, and clustering.Specific experience in Time Series Forecasting and temporal data analysis.Proficiency in Generative AI: RAG architectures, prompt optimization, and agent frameworks.Infrastructure: Experience deploying services to cloud environments (GCP preferred) and a solid grasp of MLOps and pipeline automation.Education: BS in Computer Science or related field 4 years of experience, or an MS 2 years of experience.Cultural FitStartup Agility: You possess the "scrappiness" to solve problems with limited resources but the rigor to ensure those solutions are enterprise-grade.The "Generalist" Mindset: You enjoy working across the entire stack and are not afraid to dive into data engineering or infrastructure when needed.Mission-Oriented: You are motivated by the prospect of using AI to significantly improve healthcareOur client provides a highly competitive package, including a strong base salary, meaningful equity, and comprehensive premium healthcare benefits. You will join a world-class collaborative team in a hybrid environment in Palo Alto.Please apply for more details
Hayley KillengreyHayley Killengrey
California, United States
Senior Applied AI Engineer
Applied AI Engineer (End-to-End ML)Location: Palo Alto, CA (Hybrid)Role Type: Full-Time / PermanentOur client, a pioneering HealthTech AI company in Palo Alto, is seeking a high-calibre Applied AI Engineer to bridge the gap between advanced Machine Learning and robust Software Engineering. This is an end-to-end ownership role: you will be responsible for designing the logic, building the architecture, and deploying the final services. Core ResponsibilitiesArchitect AI Workflows: Design and implement sophisticated agentic workflows and automation sequences that power clinical decision-making.System Design & Integration: Build the backend infrastructure, scalable REST APIs, and data services required to support high-concurrency AI applications.Rapid Deployment: Maintain a high-velocity shipping cycle, moving from prototype to production-grade implementation in days.Model Orchestration: Select, fine-tune, and evaluate the performance of various LLMs (including OpenAI, Anthropic, and open-source models) for specific healthcare tasks.Full-Stack ML: Own the pipeline from data ingestion and time-series forecasting to real-time classification and model monitoring.Technical ProfileComputer Science Mastery: Expert knowledge of algorithms, data structures, and distributed systems.Software-Heavy Background: Professional-grade Python skills. You should be comfortable with software design patterns, testing, and CI/CD.Machine Learning Fundamentals: * Deep understanding of Core ML topics: classification, regression, and clustering.Specific experience in Time Series Forecasting and temporal data analysis.Proficiency in Generative AI: RAG architectures, prompt optimization, and agent frameworks.Infrastructure: Experience deploying services to cloud environments (GCP preferred) and a solid grasp of MLOps and pipeline automation.Education: BS in Computer Science or related field 4 years of experience, or an MS 2 years of experience.Cultural FitStartup Agility: You possess the "scrappiness" to solve problems with limited resources but the rigor to ensure those solutions are enterprise-grade.The "Generalist" Mindset: You enjoy working across the entire stack and are not afraid to dive into data engineering or infrastructure when needed.Mission-Oriented: You are motivated by the prospect of using AI to significantly improve healthcareOur client provides a highly competitive package, including a strong base salary, meaningful equity, and comprehensive premium healthcare benefits. You will join a world-class collaborative team in a hybrid environment in Palo Alto.Please apply for more details
Hayley KillengreyHayley Killengrey