AI Engineer TSG

Burns & McDonnell; Bengaluru; On-site; INR 1,400,000 - 2,200,000; Full time

Job summary

The role emphasizes deploying on Vertex AI, Cloud Run, optimizing performance, CI/CD, and mentoring junior engineers while collaborating with cross-functional teams using Jira and GitHub.

Qualifications

• 5–8 years of experience in software engineering with strong expertise in Python.
• Hands-on experience with backend frameworks such as FastAPI and Django.
• Experience building RESTful APIs and scalable microservices.
• Strong experience with Google Cloud Platform (Vertex AI, Cloud Run, BigQuery, Cloud Storage, Dataflow, PostgreSQL).
• Experience with AI agents, Google ADK, and Model Context Protocol (MCP).
• Exposure GenAI orchestration frameworks (LangChain or similar).
• Experience working with unstructured data (PDFs, documents, engineering data).
• Knowledge of cloud cost optimization and system performance tuning.
• Experience working with LLMs and GenAI use cases (prompting, integrations, APIs).
• Hands-on experience with RAG pipelines and vector databases.
• Experience with GitHub (branching, pull requests, code reviews).
• Familiarity with Agile workflows and tools like Jira.

Responsibilities

• Design and develop scalable backend services and APIs using Python (Fast API / Django).
• Build and deploy GenAI applications including RAG pipelines, AI agents, and LLM integrations.
• Architect end-to-end solutions on GCP, covering data ingestion, processing, model integration, and deployment.
• Deploy and manage services using Cloud Run, Vertex AI, and other GCP components.
• Implement engineering best practices including CI/CD, logging, monitoring, and performance optimization.
• Collaborate with cross-functional teams and manage work through Jira.
• Use GitHub for version control, code reviews, and maintain clean, production-grade code.
• Mentor junior engineers and contribute to technical design and standards.

Skills

Python

FastAPI

Django

RESTful APIs

Microservices

Google Cloud Platform

Vertex AI

Cloud Run

BigQuery

Dataflow

PostgreSQL

LangChain

AI Agents

RAG pipelines

GitHub

Jira

CI/CD

LLMs

Education

BE (IT)

MTech

Tools

FastAPI

Django

Vertex AI

Cloud Run

BigQuery

Cloud Storage

Dataflow

PostgreSQL

LangChain

Pinecone

FAISS

GitHub

Jira

TensorFlow

PyTorch

Scikit-learn

Job description

Responsibilities

• Design and develop scalable backend services and APIs using Python (Fast API / Django).
• Build and deploy GenAI applications including RAG pipelines, AI agents, and LLM integrations.
• Architect end-to-end solutions on GCP, covering data ingestion, processing, model integration, and deployment.
• Deploy and manage services using Cloud Run, Vertex AI, and other GCP components.
• Implement engineering best practices including CI/CD, logging, monitoring, and performance optimization.
• Collaborate with cross-functional teams and manage work through Jira.
• Use GitHub for version control, code reviews, and maintain clean, production-grade code.
• Mentor junior engineers and contribute to technical design and standards.

Qualifications

• 5–8 years of experience in software engineering with strong expertise in Python.
• Hands-on experience with backend frameworks such as Fast API and Django.
• Experience building RESTful APIs and scalable microservices.
• Strong experience with Google Cloud Platform (Vertex AI, Cloud Run, BigQuery, Cloud Storage, Dataflow, PostgreSQL).
• Experience with AI agents, Google ADK, and Model Context Protocol (MCP).
• Exposure GenAI orchestration frameworks (LangChain or similar).
• Experience working with unstructured data (PDFs, documents, engineering data).
• Knowledge of cloud cost optimization and system performance tuning.
• Experience working with LLMs and GenAI use cases (prompting, integrations, APIs).
• Hands-on experience with RAG pipelines and vector databases.
• Experience with GitHub (branching, pull requests, code reviews).
• Familiarity with Agile workflows and tools like Jira.

Good to Have

• Understanding of machine learning concepts and frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with MLOps practices and model lifecycle management.

Tech Stack

Languages & Frameworks: Python, FastAPI, Django

Cloud Platform: Google Cloud Platform (Vertex AI, Cloud Run, BigQuery, Cloud Storage, Dataflow, Cloud Composer)

GenAI: LLMs, Prompt Engineering, RAG, AI Agents

Vector Databases: Vertex AI Matching Engine, Pinecone, FAISS

Tools & Platforms: GitHub, Jira

Frameworks: Google ADK, Lang Chain (or equivalent)

Qualification: BE (IT), MTech