Modernization Engineer
Kyndryl (company site, opens in a new tab)3+ yrsBengaluruOn-siteDirect career pageEst. 12–22 LPA
Posted 10h ago
Reach people at Kyndryl
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Kyndryl you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead end-to-end delivery of AI solutions using generative, agentic, and multi-agent AI technologies to solve complex customer challenges and drive measurable business value.
- Design, build, and deploy autonomous AI agents with integration across APIs, databases, cloud platforms, and orchestration tools ensuring secure, scalable, and optimized operations.
- Collaborate cross-functionally to refine AI platforms, document best practices, and translate complex business needs into effective technical solutions for executive stakeholders.
Minimum Requirements
- 3+ years experience in building AI solutions, multi-agent systems, and orchestration frameworks using tools like LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel.
- Proficient in full-stack development with Python, React.js, TypeScript; experience with microservices, RESTful APIs, SQL (PostgreSQL), NoSQL (MongoDB), and vector databases.
- Experience with Docker, Kubernetes, CI/CD pipelines (GitHub Actions), Git, and deploying AI applications on AWS, Azure, or GCP.
- Bachelor’s degree in Computer Science or Engineering (or equivalent).
Ideal Candidate Profile
- Strong blend of deep engineering expertise and consultative skills to translate business challenges into impactful AI solutions in client-facing roles.
- Experience working in hybrid environments with direct customer interaction focusing on rapid prototyping and production deployment of AI agents.
- Familiarity with advanced AI telemetry, data engineering libraries, cloud-native AI platforms, and enterprise-grade security and scalability controls for AI systems.