Senior NLP Engineer / Large Language Model Architect (offline)
Job Description
We seek a seasoned Senior NLP Engineer / Large Language Model Architect to spearhead the advancement and enhancement of our NLP initiatives. The perfect candidate will play a pivotal role in refining our vast language models, with a special focus on domains like pharmaceutical SOPs and research environments. This position demands a profound grasp of NLP fundamentals, hands-on experience in deploying extensive-scale models, and a problem-solving attitude aimed at providing top-notch, efficient solutions.
Key Responsibilities:
- Design, create, and enhance vast language models to meet precise project demands, ensuring superior quality and productivity.
- Facilitate the shift and enhancement of NLP models from platforms such as Solr and Elasticsearch to Sinequa and Amazon SageMaker.
- Formulate and execute strategies for prompt engineering, model enhancement, and training pipelines to boost model efficacy.
- Work in tandem with in-house teams and stakeholders to comprehend project requirements, offering specialist guidance and NLP architectural counsel.
- Oversee the incorporation of knowledge graphs into NLP models to bolster contextual comprehension and output pertinence.
- Assess and employ cutting-edge embedding vectors and encoding techniques to guarantee model efficacy.
- Supervise the concept extraction and entity recognition endeavors, ensuring synergy with existing knowledge graphs and data channels.
- Lead the team in broadening and polishing taxonomies using vast language models, complemented by human assessment for tagging precision.
- Promote the adoption of best practices in NLP model creation, deployment, and upkeep, staying informed of the latest sector trends and research.
Required Skills and Experience:
- Advanced degree in Computer Science, Artificial Intelligence, Linguistics, or a comparable discipline.
- Substantial experience in NLP, showcasing a robust portfolio of projects involving extensive language models.
- Competence in utilizing platforms such as Hugging Face, Sinequa, and Amazon SageMaker for model deployment and enhancement.
- Solid comprehension of model training, tuning, and enhancement methods, with a track record of applying these on large-scale endeavors.
- Expertise with knowledge graphs, entity identification, and concept extraction technologies.
- Strong coding capabilities in relevant programming languages (e.g., Python) and acquaintance with NLP libraries and frameworks.
- Proven proficiency in navigating a fast-paced, dynamic setting, managing multiple initiatives concurrently.
- Outstanding problem-solving skills, with meticulous attention to detail and a dedication to superior outcomes.
- Exceptional communication and cooperation skills, capable of elucidating complex technical concepts to non-technical audiences.
Desirable Attributes:
- Background in the pharmaceutical or research industries, with insights into SOPs and compliance demands.
- A practical stance on project management, eschewing "miraculous" fixes in favor of concrete outcomes.
- Adaptability to shifting project requirements and the capacity to guide teams through technological hurdles.
- A collaborative disposition, with experience in participating in or leading interdisciplinary teams.
Application Process:
Interested candidates are invited to submit a detailed resume/CV, alongside a cover letter highlighting your specific expertise in NLP and large language model development, and your potential contribution to our team's triumph. Please showcase examples of past projects and their influence on the organization's ambitions.
About the project & the Team:
We are transitioning from Solr and Elasticsearch on Cloudera to Sinequa and SageMaker on Amazon, not by choice, but due to circumstances. We need NLP experts for tasks related to extensive language models. Deploying the models is relatively straightforward, typically through Hugging Face, but that's not the main concern. The challenge is in the subtleties of creating high-quality extensive language models, including managing prompts, refining training pipelines, and recognizing when they succeed or fail. For instance, over the last three weeks, we tried refinement training on pharmaceutical standard operating procedures (SOPs), which was unsuccessful. Initially, the model forgot its chatbot functionalities. After two weeks of retraining on a local cluster, the enhancements on SOPs were lost again, indicating a core issue in our strategy. Our NLP team proposes experimental methods based on scholarly articles, but we lack the luxury of time for such trials.
Thus, we have a demand for experts who can offer architect-level advice on handling multiple extensive language models, including those specific to SOPs and research environments. We are developing our large language model using our data, which we plan to utilize. We need strategies on prompt engineering and ETL processes, and how to weave our knowledge graph into the prompts. Currently, we're utilizing the top embedding vector from Hugging Face for encoding, which is viable as our beta environment only hosts 20 million records, allowing for a manageable re-encoding timeline.
We aim for top-quality outcomes with our large language models, universally applicable, not just our data. We are in search of someone well-versed in the theoretical and hands-on aspects of NLP, capable of guiding us in refinement training, estimating costs, and expected quality enhancements. The goal is to engage in mature, pragmatic discussions rather than depend on uncertain, "miraculous" solutions.
Our requirement is straightforward
- An expert who can effectively deploy large language models of high quality. We're exploring various tools and methods, such as Data IKU for concept extraction and SciByte for named entity recognition (NER), supported by our knowledge graph. Our challenges include managing taxonomies, expanding synonyms with large language models, and ensuring human review for tagging accuracy during ETL. We seek a senior professional who can guide our team, which, despite some cynicism, is capable when given clear instructions.
- Our team, consisting of around 40-50 members, including 20 developers and 10 individuals focused on NLP, recently welcomed a new master's graduate, bringing fresh expertise to our algorithmic challenges. However, we lack comprehensive team capabilities. Our preference is for a homogenous team, given the challenges of managing multinational teams across different time zones.
- Our immediate need is not for Amazon or SageMaker experts but for someone to address the core aspects of large language model application. We consider a hybrid model, identifying an 'MVP' person for immediate needs, supported by a flexible structure that accommodates project-specific specialists. This approach is necessitated by our complex infrastructure, which requires a significant onboarding period to fully grasp.
- In summary, we seek a candidate capable of integrating into our system, understanding our sophisticated processes, and contributing effectively. This involves not just technical skills but also adapting to our organizational culture and procedural requirements. The ideal candidate would be evaluated over several months, allowing for a comprehensive assessment of their fit and contribution to our objectives.
We seek a seasoned Senior NLP Engineer / Large Language Model Architect to spearhead the advancement and enhancement of our NLP initiatives. The perfect candidate will play a pivotal role in refining our vast language models, with a special focus on domains like pharmaceutical SOPs and research environments. This position demands a profound grasp of NLP fundamentals, hands-on experience in deploying extensive-scale models, and a problem-solving attitude aimed at providing top-notch, efficient solutions.
Key Responsibilities:
- Design, create, and enhance vast language models to meet precise project demands, ensuring superior quality and productivity.
- Facilitate the shift and enhancement of NLP models from platforms such as Solr and Elasticsearch to Sinequa and Amazon SageMaker.
- Formulate and execute strategies for prompt engineering, model enhancement, and training pipelines to boost model efficacy.
- Work in tandem with in-house teams and stakeholders to comprehend project requirements, offering specialist guidance and NLP architectural counsel.
- Oversee the incorporation of knowledge graphs into NLP models to bolster contextual comprehension and output pertinence.
- Assess and employ cutting-edge embedding vectors and encoding techniques to guarantee model efficacy.
- Supervise the concept extraction and entity recognition endeavors, ensuring synergy with existing knowledge graphs and data channels.
- Lead the team in broadening and polishing taxonomies using vast language models, complemented by human assessment for tagging precision.
- Promote the adoption of best practices in NLP model creation, deployment, and upkeep, staying informed of the latest sector trends and research.
Required Skills and Experience:
- Advanced degree in Computer Science, Artificial Intelligence, Linguistics, or a comparable discipline.
- Substantial experience in NLP, showcasing a robust portfolio of projects involving extensive language models.
- Competence in utilizing platforms such as Hugging Face, Sinequa, and Amazon SageMaker for model deployment and enhancement.
- Solid comprehension of model training, tuning, and enhancement methods, with a track record of applying these on large-scale endeavors.
- Expertise with knowledge graphs, entity identification, and concept extraction technologies.
- Strong coding capabilities in relevant programming languages (e.g., Python) and acquaintance with NLP libraries and frameworks.
- Proven proficiency in navigating a fast-paced, dynamic setting, managing multiple initiatives concurrently.
- Outstanding problem-solving skills, with meticulous attention to detail and a dedication to superior outcomes.
- Exceptional communication and cooperation skills, capable of elucidating complex technical concepts to non-technical audiences.
Desirable Attributes:
- Background in the pharmaceutical or research industries, with insights into SOPs and compliance demands.
- A practical stance on project management, eschewing "miraculous" fixes in favor of concrete outcomes.
- Adaptability to shifting project requirements and the capacity to guide teams through technological hurdles.
- A collaborative disposition, with experience in participating in or leading interdisciplinary teams.
Application Process:
Interested candidates are invited to submit a detailed resume/CV, alongside a cover letter highlighting your specific expertise in NLP and large language model development, and your potential contribution to our team's triumph. Please showcase examples of past projects and their influence on the organization's ambitions.
About the project & the Team:
We are transitioning from Solr and Elasticsearch on Cloudera to Sinequa and SageMaker on Amazon, not by choice, but due to circumstances. We need NLP experts for tasks related to extensive language models. Deploying the models is relatively straightforward, typically through Hugging Face, but that's not the main concern. The challenge is in the subtleties of creating high-quality extensive language models, including managing prompts, refining training pipelines, and recognizing when they succeed or fail. For instance, over the last three weeks, we tried refinement training on pharmaceutical standard operating procedures (SOPs), which was unsuccessful. Initially, the model forgot its chatbot functionalities. After two weeks of retraining on a local cluster, the enhancements on SOPs were lost again, indicating a core issue in our strategy. Our NLP team proposes experimental methods based on scholarly articles, but we lack the luxury of time for such trials.
Thus, we have a demand for experts who can offer architect-level advice on handling multiple extensive language models, including those specific to SOPs and research environments. We are developing our large language model using our data, which we plan to utilize. We need strategies on prompt engineering and ETL processes, and how to weave our knowledge graph into the prompts. Currently, we're utilizing the top embedding vector from Hugging Face for encoding, which is viable as our beta environment only hosts 20 million records, allowing for a manageable re-encoding timeline.
We aim for top-quality outcomes with our large language models, universally applicable, not just our data. We are in search of someone well-versed in the theoretical and hands-on aspects of NLP, capable of guiding us in refinement training, estimating costs, and expected quality enhancements. The goal is to engage in mature, pragmatic discussions rather than depend on uncertain, "miraculous" solutions.
Our requirement is straightforward
- An expert who can effectively deploy large language models of high quality. We're exploring various tools and methods, such as Data IKU for concept extraction and SciByte for named entity recognition (NER), supported by our knowledge graph. Our challenges include managing taxonomies, expanding synonyms with large language models, and ensuring human review for tagging accuracy during ETL. We seek a senior professional who can guide our team, which, despite some cynicism, is capable when given clear instructions.
- Our team, consisting of around 40-50 members, including 20 developers and 10 individuals focused on NLP, recently welcomed a new master's graduate, bringing fresh expertise to our algorithmic challenges. However, we lack comprehensive team capabilities. Our preference is for a homogenous team, given the challenges of managing multinational teams across different time zones.
- Our immediate need is not for Amazon or SageMaker experts but for someone to address the core aspects of large language model application. We consider a hybrid model, identifying an 'MVP' person for immediate needs, supported by a flexible structure that accommodates project-specific specialists. This approach is necessitated by our complex infrastructure, which requires a significant onboarding period to fully grasp.
- In summary, we seek a candidate capable of integrating into our system, understanding our sophisticated processes, and contributing effectively. This involves not just technical skills but also adapting to our organizational culture and procedural requirements. The ideal candidate would be evaluated over several months, allowing for a comprehensive assessment of their fit and contribution to our objectives.
About That'sIT
That's IT company is a Polish-based full-cycle software development hub founded in 2014 with an R&D office in Kharkiv, Ukraine.Our main expertise is within PHP and Javascript technology, but we are not limited to the above-mentioned technologies and can set up a dedicated software development team with any tech skill set you need or you might be interested in.
Our mission is to support our customers during the whole lifecycle of the product. Starting with development of ideas and ending with the full-fledged placement of highly loaded products in the market.
Our team isn’t just developers, but creative, high-motivated, self-organized experts.
Let’s build something great together!
Company website:
http://thatsitdev.com
DOU company page:
https://jobs.dou.ua/companies/thatsit/
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