Lead Consultant - AI Application Engineering

Introduction To Role

Are you ready to turn modern AI into reliable, scalable applications that help accelerate the discovery and delivery of life-changing medicines? Do you thrive at the intersection of hands-on engineering, GenAI, and product leadership—translating ideas into tools that scientists and business teams use every day?

In this Lead Consultant - AI Application Engineering role, you will lead full-stack delivery of AI-enabled products from architecture to production, applying modern engineering practices to unlock data, automate decisions, and create seamless user experiences. You will guide multi-functional squads, shape technical direction, and ship secure, high-quality solutions that scale across a global enterprise. With strong sponsorship and access to sophisticated platforms, you will set the pace for how AI is designed, built, and adopted.

Accountabilities

• Product Architecture and Strategy: Define technical direction for AI applications, making pragmatic build-versus-buy choices and aligning roadmaps to measurable business outcomes.
• Full-Stack Delivery: Lead end-to-end engineering across front-end, back-end, APIs, data layers, and infrastructure to deliver resilient, observable, and secure applications.
• Machine Learning Pipelines: Operationalize models with robust data ingestion, feature stores, evaluation, and monitoring to ensure accuracy, reliability, and drift management.
• Generative AI Integration: Apply LLMs and related techniques (prompting, fine-tuning, retrieval) to create new user experiences and automate knowledge-heavy workflows responsibly.
• Scalable Systems: Design for performance and elasticity, optimizing cost and latency while ensuring compliance, privacy, and safe use of AI.
• Collaboration and Leadership: Partner with data scientists, product managers, and domain experts to frame problems, test hypotheses, and iterate quickly from prototype to production.
• Engineering Excellence: Uphold code quality, patterns, and standards; establish CI/CD, testing strategies, and documentation that make solutions maintainable and auditable.
• Innovation and Tech Evaluation: Maintain a clear tech radar; pilot emerging tools and methodologies and scale what works to raise engineering throughput and product impact.
• Operability and Reliability: Ensure robust SLOs, on-call readiness, telemetry, and incident playbooks so applications remain dependable at enterprise scale.
• Capability Building: Mentor engineers, grow skills in AI application engineering, and develop a culture of curiosity, perseverance, and high performance.

Essential Skills/Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• Proven experience as a Full Stack Developer with a focus on AI and machine learning applications.
• Strong proficiency in programming languages such as Python, JavaScript, or Node.js.
• Experience with frontend frameworks such as React, Angular, or Vue.js and backend technologies including RESTful APIs, Django, or Flask.
• Expertise in machine learning frameworks like PyTorch or Scikit-learn.
• Good experience with Generative AI models and techniques, such as GANs or transformers.
• Good experience with OpenAI, bedrock models, cognitive services etc.
• Experience with cloud platforms like AWS, Azure, or Google Cloud, specifically in deploying AI/ML applications.
• Strong understanding of databases such as SQL, NoSQL, and data warehousing solutions like Snowflake.
• Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
• Experience with DevOps practices and tools for continuous integration and deployment (CI/CD).
• Knowledge of microservices architectures and containerization tools like Docker and Kubernetes.
• Familiarity with version control systems such as Git etc.
• Establish and make sure development standards, security protocols, and compliance requirements across custom engineering projects.
• Conduct regular code reviews, performance evaluations, and audits to ensure the integrity and reliability of delivered solutions.
• Collaborate with IT governance teams to ensure alignment to interpersonal policies and standards.

Desirable Skills/Experience

• Experience in enterprise-level integrations and data engineering projects.
• Familiarity with data visualization tools such as Tableau or Power BI.

Why AstraZeneca

Here, engineering talent meets scientific purpose. You will work with advanced platforms, real-world data, and ambitious collaborators to deliver AI capabilities that tangibly speed research, sharpen decision-making, and expand access for patients. Backed by meaningful investment and a bold digital vision, we pair high standards with freedom to experiment—think hackathons, rapid pilots, and scaling what works—so you can build, learn, and lead. We value kindness alongside ambition, encourage transparent debate, and bring diverse expertise together to solve problems that matter at global scale.

Key skills/competency

• AI Application Engineering
• Full-Stack Development
• Generative AI
• Machine Learning Pipelines
• Cloud Platforms (AWS, Azure, GCP)
• Python, JavaScript, Node.js
• Microservices, Docker, Kubernetes
• DevOps, CI/CD
• Product Architecture
• Data Warehousing (Snowflake)