Description
At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com.
As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.
Job Function:
Data Analytics & Computational SciencesJob Sub Function:
Data ScienceJob Category:
People LeaderAll Job Posting Locations:
PENJERLA, Telangana, IndiaJob Description:
The APAC Business Technology Team within Johnson & Johnson Innovative Medicine is seeking an AI Solution Lead to design, build, and deploy scalable AI, GenAI, and data science solutions that improve commercial effectiveness, customer engagement, and business decision-making.
This role will partner closely with Business, Commercial Excellence, Marketing, and Technology teams to translate business problems into practical AI-enabled solutions. The ideal candidate combines hands-on technical capability in AI/ML, GenAI, data science, and cloud-based delivery with strong business partnership skills and the ability to drive solutions from concept to deployment.
The role is expected to contribute across the full solution lifecycle — from use case framing, data understanding, modelling, GenAI solution design, development, testing, deployment, monitoring, and continuous improvement — while ensuring responsible AI, security, compliance, and enterprise technology standards are met.
Tasks/Duties/Responsibilities
AI Solution Delivery & Technical Execution
- Build and support agentic AI solutions using patterns such as prompt engineering, RAG, tool orchestration, validation, evaluation, and monitoring
- Implement LLMOps and lifecycle governance for models and prompts, including versioning, evaluation and monitoring.
- Ensure solutions meet enterprise standards for quality, reliability, latency, and cost.
- Embed Responsible AI, security, and compliance by design, partnering with Risk, Legal, and Security.
- Evaluate emerging GenAI tools, frameworks, and methodologies and recommend practical adoption opportunities.
Engineering Excellence & DevOps
- Follow strong software engineering practices including code quality, testing, documentation, CI/CD, version control, and deployment readiness.
- Support model and application monitoring, observability, incident resolution, cost optimisation, and continuous improvement.
- Contribute to reusable assets, solution patterns, templates, and shared components to improve speed and consistency of AI delivery.
Business Partnership & Product Collaboration
- Translate business objectives into analytical and technical requirements with clear success metrics such as adoption, time saved, accuracy, quality, reliability, cost, or business impact.
- Communicate solution options, trade-offs, risks, and recommendations clearly to both business and technical stakeholders.
- Work collaboratively with product owners, data engineers, architects, and vendors to deliver end-to-end AI solutions.
Research and Innovation:
- Stay current with the latest advancements in Generative AI, cloud technologies, and LLMOps practices, and proactively translate relevant insights into team and platform adoption.
- Evaluate emerging tools, techniques, and methodologies to enhance GenAI capabilities, developer productivity, and operational efficiency, integrating them into platforms and solutions where they add value.
- Lead targeted experimentation and proofs of concept, scaling successful ideas into robust, production-ready solution
Required Years of Related Experience:
- 5+ years of experience in AI, Data Science, Machine Learning, Advanced Analytics, or Software Engineering.
- 3+ years of experience building and deploying AI/ML or GenAI solutions in production environments.
- Experience delivering solutions across the full lifecycle, from requirements gathering through deployment, monitoring, and continuous improvement.
Required Knowledge, Skills and Abilities:
- Experience designing scalable AI solution architectures across cloud, data, model, and application layers.
- Hands-on experience designing, building, deploying, and operating GenAI/agentic AI solutions including tool calling/orchestration, model evaluation, and monitoring frameworks.
- Understanding of Responsible AI, model governance, privacy, security, and enterprise risk management principles.
- Experience integrating AI solutions with enterprise applications, APIs, data platforms, and workflow systems.
- Experience implementing MLOps/LLMOps practices including model lifecycle management, prompt versioning, automated evaluation, monitoring, and governance controls.
- Practical experience with cloud and data platforms such as Databricks, Azure, AWS, Azure ML, or AWS Bedrock.
- Experience with software engineering and DevOps practices such as Git, CI/CD, testing, APIs, containerisation, workflow orchestration, and production deployment.
- Strong programming skills in Python and SQL; experience with PySpark or distributed data processing frameworks preferred.
- Ability to translate ambiguous business problems into structured analytical or technical solutions.
- Strong communication skills with the ability to explain complex AI concepts to business and technology stakeholders.
- Ability to work in a matrixed, cross-functional environment and manage multiple priorities.
- Strong ownership mindset, and problem-solving ability.
Preferred Qualifications
- Experience applying AI, data science, or advanced analytics in commercial domains such as, FMCG, retail, or e-commerce, including customer engagement, targeting, personalization, or marketing effectiveness use cases.
- Education/fundamentals in Machine learning, statistics, advance analytics, experimentation.
Required Skills:
Preferred Skills:
Advanced Analytics, Compliance Management, Critical Thinking, Data Analysis, Data Privacy Standards, Data Quality, Data Reporting, Data Savvy, Data Science, Data Visualization, Developing Others, Digital Fluency, Give Feedback, Inclusive Leadership, Leadership, Proactive Behavior, Resource Allocation, Strategic Thinking, Team Management