Key Responsibilities

Agentic AI & LLM Development

1. Design and deploy Agentic AI systems with autonomous reasoning and multi-step planning
2. Develop conversational AI agents, copilots, and RAG-based applications
3. Implement prompt engineering, evaluation frameworks, and output guardrails

AI Integration & Orchestration

1. Integrate enterprise systems using Model Context Protocol (MCP)
2. Work with multi-tool orchestration frameworks such as AgentCore

Backend & API Development

1. Develop REST APIs using FastAPI, Flask, or Spring Boot, Docker & Kubernates
2. Enable communication between AI agents and enterprise systems

Vector Databases & Semantic Search

1. Build and maintain integrations with Pinecone, Weaviate, FAISS, Milvus
2. Implement semantic search and context retrieval solutions

Cloud & Infrastructure

1. Utilize AWS services, including Amazon Bedrock, for LLM inference and deployment
2. Develop cloud-native, scalable AI systems

Machine Learning Engineering

1. Build end-to-end ML pipelines:
2. Data preprocessing
3. Feature engineering
4. Model training & evaluation
5. Deployment
6. Apply ML techniques for:
7. Customer churn prediction
8. Fraud detection
9. Network anomaly detection

Data Engineering

1. Design and maintain pipelines for structured and unstructured data
2. Work with MySQL and MongoDB

Monitoring & Optimization

1. Monitor model performance (accuracy, latency, cost)
2. Continuously improve AI/ML systems

Qualifications

Must Have

1. Bachelor’s degree in Computer Science / Data Science / IT or related field
2. Strong proficiency in Python
3. Hands-on experience with LLMs, Agentic AI systems, and orchestration workflows
4. Experience in REST API development (FastAPI, Flask, or Spring Boot)
5. Strong understanding of Machine Learning fundamentals
6. Experience with AWS (Amazon Bedrock, Docker)
7. Knowledge of SQL & NoSQL databases (MySQL, MongoDB)

Nice to Have

1. Working knowledge of JavaScript / Node.js
2. Experience in telecom-related use cases