Artificial Intelligence Engineer

Worlddevcorp Technology & Business Solutions; Pune District; In loco; INR 1.200.000 - 1.800.000; Tempo pieno

Descrizione del lavoro

World Development Corporation (WDC) in Pune invites an AI Engineer Full Stack AI, Backend & Infrastructure to own the complete AI backend lifecycle—from architecture to deployment and monitoring. You will design AI agents, build retrieval pipelines, optimize costs, and deploy production-ready systems with a strong emphasis on reliability and scalability.

The role blends AI engineering, backend development, and DevOps, requiring hands-on experience with LLMs, vector databases, and cloud

Competenze

• Backend development with Python required.
• Hands-on with LLMs in production.
• Experience with vector databases and retrieval systems.
• Knowledge of distributed systems and asynchronous processing.

Mansioni

• Design and scale multi-agent orchestration systems with 10+ agents.
• Integrate and optimize AI models, prompts, and retrieval pipelines.
• Build and maintain hybrid search pipelines (vector/keyword) and embedding workflows.
• Develop scalable backend services for AI pipelines and CI/CD in production.
• Monitor latency, reliability, and model performance; implement fallbacks.
• Balance cost, quality, and latency across AI workflows.

Conoscenze

Backend development

Python

LLM systems

Vector DBs

Distributed systems

Cloud platforms

Descrizione del lavoro

Job Description: AI Engineer Full Stack AI, Backend & Infrastructure

Company: World Development Corporation (WDC)
Department: IT
Location: Kharadi, Pune
Experience: 4+ Years
CTC: according to last drawn salary

About World Development Corporation

World Development Corporation is a leading provider of cutting-edge technologies and services, offering scalable solutions for businesses of all sizes. The organization is focused on developing high-end technologies, solutions, and processes for corporates, governments, investors, and economies.

Role Overview

We are looking for an AI Engineer Full Stack AI, Backend & Infrastructure who will own the complete AI backend lifecycle—from agent architecture and model integration to deployment, monitoring, and continuous improvement.

The role combines AI Engineering, Backend Engineering, and DevOps. You will be responsible for designing AI agents, building retrieval pipelines, optimizing costs, deploying production-ready systems, debugging issues, and making cost-versus-quality trade-offs that directly impact business outcomes.

Key Responsibilities

1. Multi-Agent System Architecture

• Design and scale multi-agent orchestration systems with 10+ specialized agents.
• Implement parallel execution, including fan-out/fan-in, conditional routing, and shared state handling.
• Build domain-specific agents with tailored retrieval, prompts, tools, and fallback strategies.
• Develop graph-based routing logic for dynamic task handling.

2. LLM Integration & Optimization

• Manage multi-model and multi-provider AI architectures.
• Select models based on cost, latency, and accuracy trade-offs.
• Build and refine prompt systems, including few-shot prompting and domain tuning.
• Optimize token usage, latency, and response quality.

3. Retrieval & Knowledge Systems

• Build hybrid search pipelines using vector search, keyword search, and metadata filtering.
• Optimize chunking, indexing, embeddings, and re-ranking strategies.
• Ensure high-quality context retrieval for LLM-powered applications.

4. Backend & Infrastructure

• Develop scalable backend services for AI pipelines.
• Implement streaming responses and asynchronous processing.
• Deploy and manage AI services using cloud infrastructure.
• Maintain CI/CD pipelines and production environments.

5. Observability & Reliability

• Build monitoring systems for latency, errors, and model performance.
• Track hallucinations, failure cases, and edge scenarios.
• Implement fallback mechanisms and reliability layers.
• Monitor and continuously improve production system stability.

6. Cost & Performance Optimization

• Optimize inference costs across models and AI workflows.
• Balance cost, quality, and latency in production systems.
• Continuously improve system performance and efficiency.

Required Skills

• Strong experience in backend development, preferably Python.
• Hands-on experience with LLMs and AI systems in production.
• Experience with vector databases and retrieval systems.
• Strong understanding of distributed systems and asynchronous processing.
• Familiarity with AWS, GCP, or Azure.
• Experience building and deploying production-ready AI applications.

Nice to Have

• Experience with multi-agent frameworks.
• Knowledge of prompt engineering at scale.
• Experience with real-time AI applications.
• Exposure to monitoring and observability tools.
• Strong ownership and accountability mindset.
• Ability to work across AI, backend, and infrastructure.
• Strong focus on shipping production-ready systems.
• Practical problem-solving approach with an emphasis on implementation over theory.

What We’re Looking For

We are looking for someone who can take end-to-end ownership of AI systems—from architecture and development to deployment, monitoring, optimization, and continuous improvement.

The ideal candidate should be comfortable working across AI, backend engineering, cloud infrastructure, and DevOps, with a strong focus on building scalable, reliable, and production-ready solutions.