GenAI Engineer - AWS

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.

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.