GCP AI Engineer

Lancesoft; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Lancesoft is seeking a Senior AI Engineer in Bengaluru to design and scale AI-powered backend systems using Python and Google Cloud Platform. You will build scalable APIs and GenAI solutions, including RAG pipelines and AI agents, with a focus on production readiness and real-world impact.

You will design end-to-end GCP architectures, deploy on Cloud Run and Vertex AI, and implement CI/CD, logging, monitoring, and performance tuning.

Qualifications

• Proficiency in Python and backend frameworks (FastAPI/Django).
• Experience building RESTful APIs and scalable microservices.
• Strong GCP experience (Vertex AI, Cloud Run, BigQuery, Cloud Storage, Dataflow, PostgreSQL).
• Experience with AI agents, MCP, and GenAI use cases.
• Exposure to GenAI orchestration frameworks (LangChain).
• Experience with unstructured data (PDFs, documents).
• Knowledge of cloud cost optimization and performance tuning.
• Experience with LLMs and GenAI integrations.

Responsibilities

• Design and develop scalable backend services and APIs using Python (FastAPI/Django).
• Build and deploy GenAI applications including RAG pipelines and AI agents.
• Architect end-to-end GCP solutions for data ingestion, processing, model integration, deployment.
• Deploy and manage services with Cloud Run, Vertex AI, and other GCP components.
• Implement 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 production-grade code.
• Mentor junior engineers and contribute to technical design and standards.

Skills

Python

FastAPI

Django

REST APIs

Microservices

GCP

Vertex AI

Cloud Run

Dataflow

BigQuery

PostgreSQL

RAG pipelines

AI agents

LangChain

LLM integrations

GitHub

Jira

Agile

Unstructured data

Tools

GitHub

Jira

LangChain

Vertex AI

Cloud Run

BigQuery

Dataflow

PostgreSQL

Job description

Ro

We are looking for a Senior AI Engineer to build and scale AI-powered applications using Python and Google Cloud Platform. This role focuses on developing backend systems, APIs, and GenAI solutions such as RAG pipelines and AI agents, with a strong emphasis on production readiness and real-world impact.

Key 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.

Required Skills & Qualifications

• 58 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).

Preferred candidate profile