AI Agent Architecture & Development :

- Design, develop, and deploy advanced tool-using AI agents capable of interacting with APIs, databases, enterprise applications, and knowledge repositories.

- Build and implement autonomous and collaborative multi-agent systems for complex business workflows.

- Develop agent communication frameworks enabling coordination and decision-making across multiple AI agents.

- Design scalable architectures for agent orchestration and task execution.

Leadership & Team Management :

- Lead and mentor a team of AI/ML Engineers and Generative AI developers.

- Drive technical design discussions, code reviews, architecture reviews, and best practices.

- Collaborate with product owners, architects, data scientists, and engineering teams to deliver enterprise-grade AI solutions.

Agent Memory Systems :

- Design and implement :

1. Short-Term Memory

2. Long-Term Memory

3. Episodic Memory

4. Retrieval-Augmented Memory Architectures

- Optimize contextual retention and reasoning capabilities of AI agents.

Multi-Agent Orchestration :

- Build fault-tolerant orchestration pipelines for multi-agent systems.

- Implement agent routing, coordination, task delegation, and workflow management.

- Design resilient recovery mechanisms and fallback strategies for agent failures.

Cloud & Platform Engineering :

- Build and deploy cloud-native AI solutions on :

1. AWS

2. Microsoft Azure

3. Google Cloud Platform (GCP)

- Develop scalable microservices and containerized AI applications.

- Work with Kubernetes, Docker, and cloud orchestration platforms.

Simulation & Testing :

- Develop testing frameworks for multi-agent interactions.

- Conduct scalability, performance, safety, and reliability testing.

- Analyze emergent behaviors and optimize agent performance.

- Implement AI evaluation metrics and benchmarking methodologies.

AI Safety, Governance & Compliance :

- Build and implement AI guardrail frameworks using :

1. Guardrails AI

2. NVIDIA NeMo Guardrails

3. Custom Validation Frameworks

- Establish responsible AI practices and policy enforcement.

- Ensure compliance with enterprise governance and regulatory standards.

Observability & Monitoring :

- Implement end-to-end observability for agent interactions.

- Build monitoring systems for :

1. Agent decisions

2. Agent performance

3. Prompt execution

4. Tool usage

5. Compliance auditing

- Develop dashboards and analytics for operational insights.

Required Skills :

AI / Machine Learning :

- Generative AI

- Agentic AI

- Multi-Agent Systems

- Large Language Models (LLMs)

- Retrieval-Augmented Generation (RAG)

- Prompt Engineering

- AI Evaluation Frameworks

Programming :

- Python

- LangChain

- LangGraph

- LlamaIndex

- FastAPI

- REST APIs

Cloud & DevOps :

- AWS / Azure / GCP

- Docker

- Kubernetes

- CI/CD Pipelines

- Microservices Architecture

Databases & Knowledge Systems :

- Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS)

- SQL / NoSQL Databases

- Knowledge Graphs

AI Governance & Security :

- Guardrails AI

- NVIDIA NeMo Guardrails

- AI Safety Frameworks

- Compliance Monitoring

- Observability Tools

Preferred Qualifications :

- Bachelor's or Master's degree in Computer Science, AI, Machine Learning, Data Science, or related discipline.

- Experience leading enterprise AI transformation initiatives.

- Hands-on experience building production-grade GenAI applications.

- Experience with distributed systems and large-scale AI deployments.

Note :

- Candidate must be available for Face-to-Face interviews at IBM locations based on account requirements.

- Candidate should be available for Day-1 reporting immediately upon onboarding confirmation