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Senior AIML Engneer

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Hybrid

Senior AIML Engneer

Blue Rock Management

Senior AIML Engneer

Skills

Google Cloud PlatformMongoDBDockerDjangoPythonMicroservicesPostgreSQLMachine LearningAIAWSKubernetesFastAPIAzure

2 months ago

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Important information


Contract type:

Permanent

Salary:

₹1,400,000 – ₹1,800,000 / year

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

10 August 2026

What they need


Sr AIML Engineer

Location: Bangalore (Hybrid 2 Days Work from Office)
Shift: General (Day Shift)
Experience: 5 - 10 Years
Role Type: Technical

Job Summary

We are seeking an experienced Senior AI Engineer to design, develop, and deploy production-grade AI/ML applications using Python, Django, and FastAPI. The ideal candidate will have expertise in building scalable backend systems, integrating Machine Learning (ML) models and Large Language Models (LLMs) into enterprise applications, and driving AI solution architecture from design through deployment.This role requires strong experience in AI Engineering, Python development, LLM integration, API design, microservices, cloud-native architectures, and secure, scalable AI platforms. The candidate will collaborate with cross-functional teams, including Data Science, Product, Infrastructure, and Security, to build enterprise-grade AI solutions.

Key Responsibilities

AI Application Development

  • Design and develop production-grade AI/ML applications using Python, Django, and FastAPI.
  • Build scalable, secure, and high-performance backend systems for AI-driven applications.
  • Develop modular, reusable, and maintainable code following software engineering best practices.
  • Translate business requirements and user stories into scalable technical solutions.

AI/ML & LLM Integration

  • Integrate Machine Learning models and Large Language Models (LLMs) into enterprise applications.
  • Design, execute, and validate ML experiments and model evaluations.
  • Extend and customize ML frameworks, libraries, and internal AI tooling.
  • Stay up to date with emerging AI, Generative AI, and LLM technologies.

Backend Engineering & Architecture

  • Design RESTful APIs and backend services using Django and FastAPI.
  • Develop cloud-native, microservices-based architectures.
  • Build scalable, secure, and highly available AI platforms.
  • Optimize application performance, scalability, and reliability.

Collaboration & Leadership

  • Work closely with Product Managers, Data Scientists, ML Engineers, Infrastructure, and Security teams.
  • Participate in solution architecture, technical design reviews, and code reviews.
  • Mentor engineers and promote coding standards, best practices, and architectural excellence.
  • Drive continuous improvement across AI engineering practices.

Mandatory Skills

  • Python
  • Django
  • FastAPI
  • AI Engineering
  • Machine Learning (ML)
  • Large Language Models (LLMs)
  • LLM Integration
  • REST APIs / API Design
  • Microservices Architecture
  • Backend Development
  • Cloud-Native Architecture
  • AI Solution Architecture
  • Software Design & Development
  • Model Validation & Testing
  • Secure & Scalable Application Development

Preferred Skills

  • Generative AI
  • Prompt Engineering
  • LangChain
  • LangGraph
  • OpenAI APIs
  • Azure OpenAI / AWS Bedrock / Google Vertex AI
  • Docker
  • Kubernetes
  • CI/CD
  • Git
  • MLOps
  • Vector Databases
  • RAG (Retrieval-Augmented Generation)
  • PostgreSQL / MongoDB
  • Cloud Platforms (AWS, Azure, GCP)

Experience

  • 510 years of software development experience.
  • Strong hands-on experience in Python backend development.
  • Experience building production AI/ML applications.
  • Experience integrating LLMs into enterprise solutions.
  • Expertise in Django, FastAPI, APIs, and microservices.
  • Experience designing scalable cloud-native architectures.
  • Strong understanding of software engineering best practices and system design.

Ideal Candidate Profile

The ideal candidate is a Senior AI Engineer with extensive experience in Python, Django, FastAPI, AI/ML, and LLM integration. They should be capable of designing scalable backend systems, building enterprise AI applications, integrating machine learning models into production environments, and mentoring engineering teams while delivering secure, high-performance AI solutions.

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