Product Leader

4 days ago


Bengaluru, Karnataka, India Axtria - Ingenious Insights Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Axtria – An Overview:

Axtria is a global provider of cloud software and data analytics to the Life Sciences industry. We help LifeSciences companies transform the product commercialization journey to drive sales growth and improvehealthcare outcomes for patients. We are acutely aware that our work impacts millions of patients and lead passionately to improve their lives.

Since our founding in 2010, technology innovation has been our winning differentiation, and we continue to leapfrog competition with platforms that deploy Artificial Intelligence and Machine Learning. Our cloud-based platforms - Axtria DataMax, Axtria InsightsIQ, Axtria SalesIQ, and Axtria MarketingIQ - enable customers to efficiently manage data, leverage data science to deliver insights for sales and marketing planning and manage end-to-end commercial operations. With customers in over 30 countries, Axtria is one of the biggest global commercial solutions providers in the Life Sciences industry. We continue to win industry recognition for growth and are featured in some of the most aspirational lists - INC 5000, Deloitte FAST 500, NJBiz FAST 50, SmartCEO Future 50, Red Herring 100, and several other growth and technology awards.

Looking for a Product Engineering Leader who has experience in building scalable B2B /B2E Products with the following experience/skills:

  • Should have experience in developing data driven and workflow-based products spanning multiple clouds (AWS/Azure/Google).
  • Should have led engineering teams to develop Enterprise-grade products which can scale on demand and are secure.
  • Ability to conceptualize products, architect /design and take them to customers in a short span of time.
  • Should have a passion for building highly scalable and performant products.
  • Apply creative thinking & approach to resolve technical solutions that further business goals and align with Product NFR goals like performance, reliability, scalability, usability, security, flexibility, and cost.
  • Conceptualize and present the vision & value of proposed architectures and solutions to a wide range of audiences in alignment with business priorities and objectives.
  • An ability to have technology centric discussions with customers, understand their requirements, manage expectations and provide solutions for their business needs.
  • Collaborate efficiently with key partners including Product managers & owners, business and engineering teams to identify and define solutions for complex business and technical requirements.
  • Any experience in Life sciences /Commercial/Incentive Compensation domain is an added plus.
  • Experience in building data centric (dealing with large data sets) products.

Behavioral Skills:

  • Product Mindset -
    Experience in agile methodology-based product development. Able to define incremental development paths for functionality to achieve future vision.
  • Task Management
    – Have team management experience and should be able to plan tasks, discuss and work on priorities.
  • Communication
    – Able to convey ideas and information clearly and accurately to self or others whether in writing or verbal.

Education & Experience:

  • 18+ Years of experience working in IT
  • 7+ years of experience in product development and core engineering
  • Bachelor's/master's degree in computer science from Tier 1-2 college.

Technology Exposures:
React JS, Python, PySpark, Snowflake, Postgres, AWS/Azure/Google Cloud, Docker, EKS. Exposure around AI/GenAI Technologies an added plus. Apart from above, even if he has good engineering experience in Java/JEE/.Net Technologies, that will also work as long as candidate has good product engineering background.

Data Engineering Experience:
Experienceworking on large-scale Data Processing systems. Data Architecture/Modelling and building Gen-AI ready datasets.

Gen-AI/AI
: Experience/Exposure building agentic solutions and workflows. Experience building RAG based solutions, semantic engines (Knowledge graphs) and overall architectural understanding of GenAI based applications and products.



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