Level

GSK

Senior Lead-AI Engineer

AI in this role

Senior Lead AI Engineer focusing on agent reliability, observability, and building machine learning and GenAI solutions.

databricksservicenowazure
prompt-engineeringai-agentsfine-tuningmachine-learninggenerative-aidata-pipelinesagent-reliability

Position Summary
This role owns agent reliability, observability, and trust controls. Monitors system health, trace logs, response quality, and hallucination signals; establishes detection thresholds and alerts. Works closely with the AI platform team to diagnose issues, improve guardrails, and ensure stable, compliant operation in production

AI Engineer will:

  • Monitor data changes in ServiceNow-SNOW (Views in Databricks) for any inconsistencies that affect agents’ reliability.

  • Controls and monitors data access points (SNOW/Azure/unstructured data, Documentation etc)

  • Consults proposed changes with Tech/Business.

  • Design and develops quick changes to data pipelines, agents, interfaces

  • Trains end users on prompt usage and best practices; tests and refines existing prompts; identifies new prompts aligned to evolving business needs. Partners with the Business Product Owner to ensure a closed-loop learning system, where user feedback, agent performance metrics, and production outcomes are systematically incorporated to improve prompt effectiveness and accuracy.

  • Ensures prompts are strictly scoped to Master Control Framework (MCF) - defined domains, controls, and measures, preventing drift into non-authoritative logic.

  • Confirms that agent responses triggered by prompts map directly to defined MCF elements such as control IDs and calculation logic. Validates reliance on MCF-authoritative data sources and enforces refusal or escalation behaviour when queries fall outside MCF scope or require human judgment.

  • Translates user feedback insights into prioritized enhancement requests.

  • Partners with Tech to improve agent usability, response clarity, and front-end experience without altering approved business logic.

  • Translates business needs into deliverable increments, and ensures alignment across architecture, security, and platform standards

  • The role combines technical knowledge and expertise (hands-on) with functional knowledge of Risk management.


Responsibilities

  • Builds and deploys machine learning and Gen AI solutions for FRMC user requirements. 

  • Develops small, easy and fast changes, automation of the maintenance tasks, data pipelines, agents, interfaces in cooperation with Business/Tech/DA Team

  • Partners with Tech and FRMC business AI governance and users to improve agent usability, response clarity, governance, and front-end experience without altering approved business logic.

  • Prioritizes and converts user feedback and business needs into deliverable increments, ensuring alignment with architecture, security, and platform standards. First line tester for any changes and enhancements.

  • Through using LLMs, and prompt engineering supports the ongoing maintenance and continuous improvement of AI agents in FRMC

  • Confirms that agent responses triggered by prompts map directly to defined master control framework, controls testing, and risk and control process elements such as control IDs and calculation logic according to the AI agent’s role.

  • Controls and monitors data access points (SNOW/CO/unstructured data, GSOPs etc) Monitor process (business) and/or technology changes to act promptly without interruption of the solution.

  • Ensure documentation, trainings, governance rules (Tech Azure governance) are updated and available Partners with the Business Product Owner to ensure a closed-loop learning system, where user feedback, agent performance metrics, and production outcomes are systematically incorporated to improve prompt effectiveness and accuracy.

  • Maintain and share a detailed understanding of the underlying service landscape and the tools/platforms used 

  • Validates reliance on FRMC AI agents -authoritative data sources and enforces refusal or escalation behaviour when queries fall outside MCF scope or require human judgment.

Basic Qualification
We are seeking professionals with the following required skills and qualifications to help us achieve our goals:

  • Bachelor’s degree in computer science, engineering, data science or related field.

  • 5+ years of hands-on experience building and/or modifying Azure Web applications (Azure Webapps) and use of Azure AI solutions

  • Strong Python skills and experience with AI frameworks (Lang*) + FastAPI, Kong, Pydantic, Spark

  • Experience with MS Azure products and concepts (Databricks, ADF, Genie, WebApps, AppServices, KeyVault etc) and containerization (Docker, Kubernetes).

  • Solid understanding of data engineering: SQL, views, blob storage, data models

  • Experience using GitHub, *.Json,*.Js, Jupyter, *.yaml, *.sh

  • Understanding of the Azure deployment process

  • Good communication and collaboration skills for working in cross-functional teams.


Additional Qualification
If you have the following characteristics, it would be a plus:
- Master’s degree or higher in software engineering field.
- Experience in finance/Finance controlling/Financial Risk management environment

- Experience with creating AI agents in Azure environment using Python/WebApps
- Knowledge of large language models, generative AI and fine-tuning techniques.

- Experience in working with unstructured data (vectorisations etc)
- Familiarity with model governance, privacy-preserving techniques and bias mitigation.
- Experience working in highly regulated, international and distributed environments.

 


Skills

Business Forecasting, Business Reporting Tools, Data Storytelling, Digital Fluency, Financial Modeling, Financial Performance Improvement, Influencing Without Authority, Insight Generation, Problem Solving

 

 

Why GSK?

Uniting science, technology and talent to get ahead of disease together.

GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.

People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.

Inclusion at GSK:

As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process.

Please contact our Recruitment Team at IN.recruitment-adjustments@gsk.com to discuss your needs.

Important notice to Employment businesses/ Agencies

GSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site.

It has come to our attention that the names of GlaxoSmithKline or GSK or our group companies are being used in connection with bogus job advertisements or through unsolicited emails asking candidates to make some payments for recruitment opportunities and interview. Please be advised that such advertisements and emails are not connected with the GlaxoSmithKline group in any way.

GlaxoSmithKline does not charge any fee whatsoever for recruitment process. Please do not make payments to any individuals / entities in connection with recruitment with any GlaxoSmithKline (or GSK) group company at any worldwide location. Even if they claim that the money is refundable.

If you come across unsolicited email from email addresses not ending in gsk.com or job advertisements which state that you should contact an email address that does not end in “gsk.com”, you should disregard the same and inform us by emailing askus@gsk.com, so that we can confirm to you if the job is genuine.

 

How we rate this

Senior Lead-AI Engineer at GSK rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringAI AgentsFine TuningMachine LearningGenerative AIData PipelinesAgent ReliabilityDatabricks

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  4. Tell me about a project where machine learning was part of your work. What did you do?
  5. Tell me about a project where generative ai was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Prompt Engineering, AI Agents, Fine Tuning, Machine Learning, and Generative AI. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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