Associate Director, Software Engineering- Scientific & AI Tools at AstraZeneca

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

11 Apr, 26

Salary

0.0

Posted On

11 Jan, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, R, Java, C++, Data Engineering, Visualization, Streamlit, Dash, React, FastAPI, Flask, SQL, NoSQL, AI, Machine Learning, Cloud

Industry

Pharmaceutical Manufacturing

Description

Job Title: Associate Director, Software Engineering- Scientific & AI Tools Global Career Level: E About our Team: AstraZeneca is transforming into an AI- and data-led enterprise. Within R&D, our Predictive AI & Data team connects expertise across functions to turn complex information into practical, life-changing insights that improve patient outcomes. We invent, build, and deliver novel solutions alongside leading experts, leveraging cutting-edge techniques in data, AI, and machine learning. We work inclusively across diverse disciplines and partners, pooling knowledge to decode business needs, and applying our technical knowledge to deliver value. Introduction to role: We are seeking an Associate Director, Software Engineering to lead the design and delivery of Scientific & AI tools and solutions that accelerate scientific decision-making across Clinical Pharmacology & Safety Science (CPSS). You will shape and deliver scalable, production-quality software on enterprise infrastructure, driving positive disruptive transformation aligned to AstraZeneca’s Bold Ambition for 2030. This role partners closely with R&D IT and DS&AI and collaborates globally with colleagues in Sweden, the United Kingdom, and the United States. Accountabilities: Product leadership: Translate scientific and business requirements into technical roadmaps; own delivery of fit-for-purpose tools end-to-end. Architecture and development: Design, build, and operate scalable software in scientific, data, and AI domains, applying modern engineering best practices. AI solutions: Implement Large Language Model–powered systems, Retrieval-Augmented Generation (RAG), and agentic workflows to enhance scientist productivity. Data platforms: Build data ingestion, transformation, and visualization solutions; ensure data quality, security, lineage, and governance. Collaboration: Partner with CPSS stakeholders, R&D IT, and DS&AI to co-create solutions; provide clear communication of complex topics to diverse audiences. Operational excellence: Establish CI/CD, automated testing, observability, and DevOps practices; ensure reliability, performance, and cost efficiency in cloud and HPC environments. Technical mentorship: Guide engineers and contributors; foster a culture of craftsmanship, learning, and inclusive collaboration. Essential Skills/Experience: Education: Degree in Computer Science, Engineering, or related field, or equivalent industry experience. Minimum 10+ years of relevant experience. Programming: Strong Python skills; familiarity with R, Java, or C++. Software engineering: Proven delivery of scalable, production-quality systems; proficiency with version control, CI/CD, automated testing, design patterns, and DevOps. Data engineering and visualization: Experience building pipelines and interactive data products. Frontend: Experience with Streamlit, Dash, or React. Backend: Experience with FastAPI or Flask. Databases: Experience with SQL and NoSQL technologies. Generative and agentic AI: Practical experience with frameworks such as PydanticAI, LangChain and LangGraph; knowledge of LLMs, RAG, and prompting techniques. Compute environments: Hands-on experience with Unix/Linux HPC systems and cloud platforms (AWS preferred). Translation of needs: Demonstrated ability to convert scientific and business requirements into robust technical solutions. Core skills: Excellent problem-solving, analytical, and critical-thinking skills; attention to detail and strong communication and interpersonal skills. Desirable Skills/Experience: Domain expertise: Background in machine learning, AI, or bioinformatics. Architecture: Experience designing data platforms and agentic AI solutions. Life sciences: Experience in clinical or pre-clinical drug discovery. Scientific computing: Knowledge of scientific methods, pipelines, and computational tool development. When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world. Why AstraZeneca: Here, you’ll blend cutting-edge engineering with real-world science to push the boundaries of what’s possible—fusing data, technology, and bold ideas to accelerate breakthroughs against complex diseases. We bring unexpected teams together, learn relentlessly, and support each other with kindness alongside ambition. You’ll work on challenges that have never been solved before, grow through varied opportunities, and see the impact of your tools in publications, decisions, and medicines that reach patients worldwide. Call to Action: If you’re ready to lead engineering that turns scientific curiosity into life-changing impact, take the next step and show us how you will build the tools that speed breakthroughs to patients! Date Posted 09-Jan-2026 Closing Date AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.

Responsibilities

Lead the design and delivery of Scientific & AI tools and solutions to accelerate scientific decision-making. Collaborate with stakeholders to translate scientific and business requirements into technical roadmaps and deliver fit-for-purpose tools.