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GenAI Engineer - AWS

Safran -
Bengaluru, Karnataka

Apply Now

Job details

Permanent | Full-time

Qualifications

CI/CD
Elasticsearch
Azure
Law
Kubernetes
Software deployment
Encryption
Git
Master's degree
AWS
Docker
Bachelor's degree
Terraform
Continuous integration
REST
GitHub
APIs
S3
GraphQL
Flask
Identity & access management

Full job description

Published 12.17.2025

Company : Safran Engineering Services Job field : Software Location : Bangalore , India Contract type : Permanent Contract duration : Full-time Required degree : Bachelor's Degree Required experience : More than 5 years Professional status : Employees / Staff

# 2025-166712

Published 12.17.2025

Job Description

We are seeking a experienced Generative AI engineer to lead the design and development of an AI platform that leverages Large Language Models (LLMs) and other cutting-edge AI technologies. In this role, your core responsibilities are

Architect and build GenAI-powered applications using LLMs and advanced RAG pipelines for technical documentation, maintenance, and support workflows.
Design and implement retrieval-augmented generation (RAG) systems leveraging vector databases and semantic search for contextual information retrieval.
Integrate and fine-tune LLMs (OpenAI, Anthropic, Amazon Bedrock, HuggingFace, etc.) for domain-specific tasks and continuous improvement.
Develop robust evaluation frameworks for LLM output (relevancy, faithfulness, summarization, contextual accuracy).
Build and deploy microservices and APIs for GenAI solutions, ensuring high availability, scalability, and secure access.
Collaborate with product, domain, and platform teams to refine requirements and deliver innovative GenAI solutions.
Stay current with GenAI trends, frameworks, and best practices to future-proof solutions for evolving enterprise needs.

Job Requirements

Required Technical Skills

GenAI & LLMs: OpenAI, Anthropic, Amazon Bedrock, HuggingFace Transformers, LangChain, RAG architectures, prompt engineering, fine-tuning.
Vector Databases & Semantic Search: Pinecone, FAISS, Milvus, Weaviate, Elasticsearch/OpenSearch with vector support.
GenAI Evaluation & Monitoring: G-Eval, Prometheus, QAG scorer, MLflow, DataDog, CloudWatch, custom LLM-as-a-judge frameworks.
API & Microservices: RESTful API design, GraphQL, FastAPI, Flask, OAuth2/JWT, containerization (Docker, Kubernetes).
Security & Compliance: IAM, KMS, VPC endpoints, S3 Object Lock, CloudTrail, encryption policies, GDPR/industry compliance.
DevOps for GenAI: CI/CD pipelines (GitHub Actions, CodePipeline), Terraform, AWS CDK, CloudFormation for automated deployment and scaling of GenAI services.
Data Modeling for GenAI: Metadata tagging, schema design, data quality management for LLM training and inference.
Multi-modal Retrieval: Experience with text, images, video, telemetry for comprehensive GenAI solutions.

Preferred Experience

Building enterprise-grade RAG systems for technical documentation, support, or maintenance workflows.
Implementing multi-modal retrieval in large-scale environments.
Familiarity with continuous evaluation and improvement of LLMs in production (A/B testing, feedback loops).
Exposure to regulated industries (aviation, manufacturing, healthcare) with strict compliance and audit requirements.
Working with multi-cloud architectures (AWS, Azure, GCP) and hybrid deployments.
Advanced knowledge of MLOps: model deployment, monitoring, rollback, and lifecycle management.

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