Resident Engineer, deepset
+ Stock option plan
Open source NLP framework
Open for applications
Open source NLP framework
21-100 employees
Open for applications
+ Stock option plan
21-100 employees
Company mission
To empower developers to make Natural Language Processing (NLP) part of any application in any enterprise.
Role
Who you are
- Proven experience in AI, NLP, ML engineering, or technical customer-facing roles (e.g., Solution Architect, ML Engineer, NLP Engineer)
- Demonstrable experience leading end-to-end projects, with strong Project Management principles
- Expertise in NLP, LLMs, and retrieval-augmented generation (RAG) architectures
- Strong experience with Haystack, deepset Cloud, Elasticsearch, OpenSearch, or similar search technologies
- Proficiency in Python, APIs, and containerization (Docker, Kubernetes)
- Experience with cloud platforms (AWS, GCP, Azure) and deploying ML models in production
- Experience with On Premise (bare metal and VPC) deployments
- Ability to work with LLMs and fine-tune models for domain-specific applications
- Experience with integrating AI solutions with enterprise IT architectures
- Excellent communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders
- Ability to work independently within a customer’s environment while collaborating with deepset’s internal teams
What the job involves
- You’ll play a key role in the success of deepset AI Platform, our SaaS platform for Large Language Model (LLM) applications, empowering users to build their own LLM-powered features
- You will be our very first Resident Engineer supporting the growth of our enterprise clients, therefore, this person will be instrumental in helping to define and architect the impact and scope of this position - an awesome opportunity for the right curious individual
- Act as a dedicated technical subject matter expert (SME) and trusted advisor for our largest customer’s on-premise and cloud-based implementations, ensuring seamless integration of Haystack and deepset AI Platform into customer ecosystems
- Provide on-site (or remote) support, troubleshooting, and guidance for deployments, integrations, and performance optimisation
- Collaborate with customer engineering, product, DevOps and delivery teams to ensure smooth adoption and usage of the product; including project management of Customer and deepset resources
- Deliver workshops, technical enablement, and best-practice documentation to upskill the customer’s technical teams
- Work closely with internal teams (Customer Success, Professional Services, Support, and Product) to escalate and resolve technical issues
- Help drive customer adoption, retention, and expansion through technical excellence, strategic guidance, training, enablement and hackathons
- Provide deepset and AI COE (Center of Excellence) setup and governance guidance
- You'll join our cross-functional Product team, and work closely with Product, Design and other engineers
- You’ll contribute to building a cutting-edge AI platform capable of handling high data volumes, enabling users to deploy scalable AI applications that manage large-scale traffic efficiently.
Our take
Natural Language Processing (NLP) is at the core of many modern enterprise data processing workflows, from complex data processes to customer service chatbots. However, NLP systems are not one-size-fits-all, and the development and deployment of bespoke NLP systems require the niche and expensive help of programmers with expertise in machine learning and semantic syntax.
Deepset provides an open-source API-driven framework called Haystack for the quick and efficient development of production-ready NLP systems. The company's goal is to make modern NLP as easy as possible to apply in a practical and scalable way, and states major use cases of its API for automated data analysis, customer satisfaction, fraud monitoring, and regulatory actions.
Whilst NLP has been implemented by many enterprises such as Amazon AWS and Microsoft, options for companies without dedicated AI experts have been limited, making deepset's open-source approach highly beneficial to many industries. Lately, the company has invested funding in the development of a SaaS product called deepset Cloud, which will enable users to build complex LLM apps more easily and at "a fraction of the time".
Steph
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Aug 2023
$30m
EARLY VC
Apr 2022
$14m
SERIES A
Total funding: $45.6m
Company benefits
- Remote-first - you can choose to work from home or any other workspace that suits you
- Home office budget for equipment and furniture, and we would be happy to sponsor a coworking spot
- Annual learning and development budget
- Opportunity to speak at conferences
- Internal knowledge sharing and workshops
- Virtual office
- Monthly social budget to reconnect virtually or in person
- Online walks and talks with colleagues from other teams
- Flexible travel policy that enables you to meet colleagues in person for special occasions
- Quarterly in-person meet up - we have fruitful discussions, we hold workshops, we brainstorm together, and we have leisure activities and fun
- Employee stock option plan
- 27 days of paid vacation, and you earn an additional day for each year completed with deepset (up to 30 days)
- Choose your tech - Linux, Windows, or macOS? The choice is yours and we will provide what you need
- Pension plans
- Travel insurance
- Monthly allowance for your sports activity
- Dog-friendly - our main co-working partner Mindspace welcomes dogs, so you can bring your best friend to the office
- Enhanced days off for Family Sick Leave
- Additional days off - we don't work on the 24th & the 31st of December
- Flexible working hours
Company HQ
Kreuzberg, Berlin, BE
Leadership
Milos Rusic
(Co-founder & CEO)Experience as Director of Market Development for KONUX from 2016 to 2018.
Malte Pietsch
(Co-founder & CTO)Experience as Data Scientist for plista from 2016 to 2018.
Timo Möller
(Co-founder)Experience as Machine Learning Engineer for plista from 2016 to 2017.
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