
Senior Data Scientist - ST&S
Appcast EnterpriseEntity:
Technology
Job Family Group:
Job Description:
Our purpose is to deliver energy to the world, today and tomorrow. For over 100 years, bp has focused on discovering, developing, and producing oil and gas in the nations where we operate. We are one of the few companies globally that can provide governments and customers with an integrated energy offering. Delivering our strategy sustainably is fundamental to achieving our ambition to be a net zero company by 2050 or sooner!
Summary
We’re looking for engineers who think holistically, automate relentlessly, and are fluent in the fast-moving world of AI tooling and infrastructure—but grounded in disciplined engineering principles.
Our AI organization is building high-impact AI-powered applications that deliver real business value at speed. As a Senior AI Engineer, you’ll play a critical role in building and deploying scalable AI-powered applications through solid software engineering excellence combined with pragmatic use of modern AI capabilities. This is a role for seasoned engineers who are excited about applying AI in practical and scalable ways.
We’re looking for individuals who thrive at the intersection of disciplined software development and modern AI applications. You should be comfortable working across the full lifecycle of a product—from ideation and architecture to deployment and automation—while navigating ambiguity and driving toward execution. Strong systems thinking, ownership mindset, and the ability to ship value fast are essential.
Job Responsibilities
- Design, develop, and maintain production-grade AI applications and services using modern software engineering practices (CI/CD, testing, observability, cloud-native design).
- Define and implement foundational platforms and tools (e.g., conversational bots, AI-powered search, unstructured data processing, GenBI) that are reusable and scalable across the enterprise.
- Participate in cross functional team initiatives—embedded projects with business stakeholders—to rapidly build and deploy AI solutions that solve high-priority business problems.
- Evaluate and integrate existing AI tools, frameworks, and APIs (e.g., LLMs, vector DBs, retrieval-augmented generation, AI agents) into robust applications.
- Champion automation in workflows—from data management ingestion and preprocessing to evaluation, to model integration and deployment.
- Collaborate with data scientists, product managers, and other engineers to ensure end-to-end delivery and reliability of AI products.
- Stay current with emerging AI technologies, but prioritize practical application and delivery over experimental research.
- Contribute to the internal knowledge base, tooling libraries, and documentation to scale AI engineering best practices across the organization.
Job Qualifications
- Proven track record of professional software engineering experience; ability to independently design and ship complex systems in production.
- Strong programming skills in Python (preferred), Java, or similar languages, with experience in developing microservices, APIs, and backend systems.
- Strong problem-solving skills and the ability to balance engineering rigor with delivery speed.
- Solid understanding of software architecture, cloud infrastructure (AWS, Azure, or GCP), and modern DevOps practices.
- Experience integrating machine learning models into production systems (e.g., LLMs via APIs, fine-tuning, RAG patterns, embeddings, agents and crew of agents etc.).
- Ability to move quickly while maintaining code quality, test coverage, and operational excellence. Preferred:
- Familiarity with AI/ML tools such as LangChain, Haystack, Hugging Face, Weaviate, or similar ecosystems.
- Hands-on experience with Retrieval Augmented Generation applications, AI agents and systems built around them.
- Experience using GenAI frameworks such as LlamaIndex, Crew AI, AutoGen, or similar agentic/LLM orchestration toolkits.
- Exposure to working with unstructured data (documents, conversations, images) and transforming it into usable structured formats.
- Experience building chatbots, search systems, or generative AI interfaces.
- Background in working within platform engineering or internal developer tools teams.
Prior experience working in an embedded (forward-deployed) team model with business stakeholders.
- Experience building production grade, reliable AI applications
Why join us?
At bp, we support our people to grow in a diverse and exciting environment. We believe that our team is strengthened by diversity.
There are many aspects of our employees’ lives that are meaningful, so we offer benefits to enable your work to fit with your life. These benefits can include flexible working options, a generous paid parental leave policy, excellent retirement benefits, among others!
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Reinvent your career as you help our business meet the challenges of the future. Apply now!
Travel Requirement
Relocation Assistance:
Remote Type:
Skills:
Legal Disclaimer:
Opens the company's application page
Listed via
Reed
reed.co.uk
Similar roles
Senior Machine Learning Scientist, FinCrime
Monzo

Machine Learning Engineer- World-Leading Prop Trading Fund
eFinancialCareers
Senior Machine Learning Engineer (m/f/*)
Peregrine.ai

Data Scientist - Principal - Aristocrat
eFinancialCareers
Design & Tech
Related reads from TCHNX

Why AI Design Tools Are Quietly Replacing Junior Designers and What Actually Comes Next
AI tools promise efficiency, but London studios are discovering an unexpected paradox: automation creates new bottlenecks requiring precisely the expertise being eliminated. We investigate what's actually happening to entry-level design work.

The Inference Economy: Why AI’s Biggest Cost Shift Is Happening After Training
A major shift in AI economics is reshaping the industry. As training frontier models becomes more expensive and inference becomes dramatically cheaper, companies are being forced to rethink how they build, deploy, price, and monetise intelligent systems.

The Emergence of Small Language Models: Why Efficiency Is Overtaking Scale
As the AI industry confronts computational costs and environmental concerns, a new generation of compact models is proving that bigger isn't always better. Small language models are reshaping enterprise AI deployment.