AI Customer Engineer

Eligibility Criteria & Experience

Hands-on experience with Google ADK or CoPilot Studio in building AI Agents (or CrewAI, Autogen).

Hands-on working knowledge in defining and configuring prompts, instructions, tools, reasoning, and guardrails.

Strong experience with RAG pipelines, Vector DBs, tokenization, and prompt engineering.

Hands-on experience in creating and maintaining Python libraries using LangChain, Hugging Face, OpenAI API, or local models.

Very strong experience with developing RESTful APIs in Python using frameworks like FastAPI.

Hands-on working with Docker-based deployments and leveraging GitHub for code repo and version control is MUST.

Role Overview & Job Description

As an AI Customer Engineer, you will make an impact by building scalable AI agent solutions and accelerating enterprise adoption of Generative AI technologies.

You will be a valued member of the AI Engineering team and work collaboratively with architects and product teams to deliver production-grade AI applications.

Key Responsibilities

Build and deploy AI agents and GenAI-powered applications using modern frameworks and platforms.

Design and implement RAG pipelines, prompt engineering workflows, and LLM integrations.

Develop scalable backend services and APIs for AI-driven systems.

Enable cloud-native deployments with containerization and microservices architecture.

Collaborate with cross-functional teams to deliver reliable, secure, and high-performing AI solutions.

Required Technical & Soft Skills

Technical: Google ADK / CoPilot Studio, RAG pipelines, Vector DBs, Python, FastAPI, Docker, GitHub, Microservices.

Soft Skills: Strong communication and articulation, Cross-functional collaboration.

Important Notes & Details

Work Mode: Hybrid (Onsite position requiring 5 days a week)

Shift Details: Regular business hours

Applicants may be required to attend interviews in person or by video conference.