Hybrid AI Developer

at Kyndryl

• Job title: AI Developer
• Job type: Full Time
• Experience level: Senior, Lead
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Cloud, Distributed Systems, Python
• Location requirements: Hybrid, Bangalore, India

About the role

• Senior Agentic AI Developer at Kyndryl to build autonomous AI systems for complex tasks. Collaborating to integrate LLMs and achieve real-world business outcomes.

Responsibilities

• Design and implement agent-based AI systems that can reason, plan, act, and self‑correct
• Build multi‑agent workflows (collaborative, hierarchical, or tool‑using agents)
• Implement memory, state management, tool orchestration, and long‑running agent processes
• Optimize prompt engineering, reasoning strategies, and evaluation pipelines
• Integrate and fine‑tune LLMs (open‑source and commercial) for agentic use cases
• Apply techniques such as RAG, function calling, planning loops, reflection, and self‑critique
• Evaluate model performance, reliability, safety, and cost trade‑offs
• Stay current with advances in agentic frameworks and AI research
• Build scalable, production‑grade AI services and APIs
• Design secure, fault‑tolerant, and observable systems
• Collaborate on system architecture, data pipelines, and MLOps practices
• Ensure high code quality through testing, reviews, and documentation
• Partner with product managers, UX, data scientists, and platform teams
• Mentor junior engineers and contribute to engineering best practices
• Translate business problems into agentic AI solutions
• Influence technical direction and roadmap for AI capabilities

Requirements

• 7+ years of professional software development experience, with 3+ years in AI/ML
• Strong proficiency in Python (primary) and experience with backend frameworks
• Hands‑on experience with LLMs and agentic AI frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar)
• Experience with RAG systems, vector databases, and embedding pipelines
• Solid understanding of distributed systems, APIs, and cloud‑native architectures
• Familiarity with CI/CD, monitoring, and MLOps practices
• Strong understanding of NLP, prompt design, and evaluation techniques
• Experience working with structured and unstructured data
• Knowledge of model safety, hallucination mitigation, and governance.

Benefits

• Flexible work arrangements
• Professional development opportunities
• Health insurance
• Wellness programs