Sr Gen AI Engineer

Safran Engineering Services; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Safran Engineering Services in Bengaluru is seeking a Sr. GenAI Engineer to lead the design and development of an AI platform that leverages Large Language Models and cutting-edge AI technologies.

You will architect GenAI-powered applications, implement RAG pipelines, and collaborate with cross-functional teams to deliver scalable solutions. The role requires 7 to 12 years of experience, hands-on expertise in model deployment, retrieval, and multi-modal retrieval, and a strong focus on security,

Qualifications

• GenAI & LLMs experience with OpenAI, Anthropic, Bedrock, HuggingFace, LangChain, RAG, and prompting.

Responsibilities

• Architect and build GenAI-powered apps using LLMs and RAG for docs, maintenance, and support workflows.
• Design RAG systems with vector databases and semantic search for contextual retrieval.
• Integrate and fine-tune LLMs for domain tasks and continuous improvement.
• Develop evaluation frameworks for LLM output (relevancy, faithfulness, summarization).
• Build and deploy microservices and APIs with high availability and secure access.
• Collaborate with product, domain, and platform teams to refine requirements.
• Stay current with GenAI trends and best practices to future-proof solutions.

Skills

GenAI & LLMs

RAG pipelines

Vector Databases

Semantic Search

Prompt Engineering

MLOps

APIs & Microservices

Multi-modal Retrieval

Cloud & Hybrid Deployments

Security & Compliance

Tools

Pinecone

FAISS

Milvus

Weaviate

Elasticsearch/OpenSearch

Docker

Kubernetes

FastAPI

GraphQL

OAuth2/JWT

Job description

Role

Sr. GenAI Engineer

Location

Bangalore

Experience

7 to 12 Years

Key Responsibilities

We are seeking a experienced Generative AI developer 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.
• Prompt Engineering and design of 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

Candidate Skills & Requirements

• 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