Artificial Intelligence Engineer

GyanSys Inc.; Bengaluru; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

GyanSys Inc. in Bangalore (Hybrid) seeks an AI Engineer to build production-grade agentic AI systems for enterprise use. You will develop multi-agent workflows, integrate LLMs, and ensure reliable, secure deployments in production.

Responsibilities include building AI agents with LangGraph, tuning prompts across GPT, Claude, and LLaMA, and delivering scalable REST APIs and event-driven pipelines. Strong Python and CI/CD skills are required.

Qualifications

• 3–5 years in ML/AI with production systems.
• At least 1 year building agentic AI apps.
• Strong Python and modern development practices.
• Hands-on with LLMs and prompt engineering.
• Experience building AI agents with LangGraph.
• Familiar with enterprise cloud AI platforms incl. deployment.
• Knowledge of REST APIs, WebSockets, event-driven systems.
• CI/CD tooling (Jenkins) and Git.
• Strong communication and independent work in cross-functional teams.

Responsibilities

• Build AI agents and multi-agent systems using LangGraph and LangChain.
• Develop and tune prompt workflows across multiple LLMs (GPT, Claude, LLaMA).
• Develop REST APIs, WebSocket services, and event-driven pipelines for real-time AI services.
• Automate testing and releases through Jenkins CI/CD, and maintain code/docs with Git/Jira.
• Coordinate with platform, security, and product teams for scalable deployments.

Skills

Python

Agentic AI

LLM Prompting

LangGraph

REST/WebSocket

Jenkins CI/CD

Git

Cross-functional work

Cloud AI platforms

Problem solving

Tools

LangChain

Azure AI Foundry

AWS Bedrock

Google Gemini

Databricks

Kubernetes

Job description

Location: Bangalore (Working from Office / Hybrid)

Job Description

We are seeking a AI Engineer to build and deliver production-grade agentic AI systems for enterprise use. The engineer will develop multi-agent workflows, integrate large language models into existing enterprise systems, and support the deployment and automation needed to run them reliably and securely in production.

This is a hands-on engineering engagement. The work centers on building agents, orchestration logic, and supporting infrastructure that performs under real production workloads, not on proof-of-concept or advisory work.

Scope of Work

Build AI agents and multi-agent systems using frameworks with LangGraph and LangChain tools.

Develop and tune prompt engineering workflows across multiple LLMs (GPT, Claude, LLaMA), balancing reliability, cost, and latency.

Develop REST APIs, WebSocket services, and event-driven pipelines for real-time AI services that remain stable under load.

Automate testing and releases through Jenkins CI/CD, and maintain code and documentation standards using Git, Jira, and Confluence.

Deployment of AI Application in enterprise adhering to best practices

Use AI-augmented development tools such as Claude Code and Codex to accelerate delivery. Coordinate with platform, security, and product teams to deliver scalable, secure deployments.

Must-Have Skills

3-5 years in Machine Learning, AI, or a related field, with production systems delivered.

At least 1 year building custom Agentic AI applications

Strong Python skills and sound modern development practices.

Hands-on experience with LLMs and prompt engineering across the full application lifecycle.

Demonstrated experience building AI agents with LangGraph.

Familiarity with at least one enterprise cloud AI platform for building and deploying agentic applications, such as Azure AI Foundry, AWS Bedrock, or Google Gemini Enterprise, including cloud-native deployment practices.

Working knowledge of REST APIs, WebSockets, and event-driven systems.

Proficiency with CI/CD tooling (Jenkins) and version control (Git).

Fluency with AI-augmented development tools for rapid prototyping.

Strong written and verbal communication, an analytical approach to problem-solving, and the ability to work independently within a cross-functional team.

Data layer curations and integration with source system for agentic application

Good-to-Have Skills

Familiarity with Databricks.

Exposure to MLOps/LLMOps workflows and application monitoring.

Knowledge of enterprise security, compliance, and governance for AI systems.

Familiarity with code and model lifecycle management practices.