AWS GenAI Engineer

Role Summary

We are looking for an AWS GenAI Engineer to design, build, and scale Generative AI and Agentic AI solutions on AWS. The role involves working across LLMs, RAG pipelines, AI agents, MLOps, and cloud-native architectures, enabling enterprise-grade AI use cases such as copilots, intelligent assistants, document intelligence, and AI-powered automation.

Key Responsibilities

• Design and develop Generative AI solutions on AWS using managed and open-source LLMs
• Build RAG (Retrieval Augmented Generation) pipelines using enterprise data sources
• Develop AI agents and copilots for business, engineering, and operations use cases
• Integrate LLMs with applications, APIs, and enterprise systems
• Implement prompt engineering, fine-tuning, and model optimization strategies
• Operationalize GenAI solutions using MLOps / LLMOps best practices
• Ensure security, compliance, and data privacy in AI implementations
• Collaborate with architects, data engineers, and business stakeholders to translate use cases into scalable solutions
• Support POCs, pilots, and industrialization of GenAI capabilities
• Successfully delivered GenAI use cases from POC to production

Core Technical Skills

Generative AI & LLMs

• Hands-on experience with LLMs (e.g., Amazon Bedrock models, OpenAI-compatible models, open-source LLMs)
• Prompt engineering, prompt chaining, and context optimization
• RAG architecture design (embeddings, vector stores, retrieval strategies)
• Knowledge of fine-tuning vs prompt tuning vs adapters
• Amazon Bedrock (model selection, guardrails, orchestration)
• AWS SageMaker (training, inference, pipelines)
• AWS Lambda, ECS/EKS, Step Functions
• S3, DynamoDB, Aurora / RDS
• IAM, KMS, VPC, CloudWatch for secure AI workloads
• Experience with serverless and container-based AI architectures
• Strong proficiency in Python
• Familiarity with LangChain / LlamaIndex / Semantic Kernel
• REST APIs, FastAPI, Flask
• Experience integrating LLMs with web and enterprise applications
• Embeddings and semantic search
• Vector databases such as OpenSearch, Pinecone, FAISS, Chroma
• Data preprocessing, chunking, and metadata strategies

Nice-to-Have Skills

• Agentic AI frameworks and multi-agent orchestration
• Integration with RPA / workflow engines
• Knowledge of DevOps & IaC (Terraform / CloudFormation)
• Experience in manufacturing, enterprise IT, or large-scale transformation programs
• Exposure to Copilot-style application development
• CI/CD for AI models and prompts
• Model versioning, monitoring, observability
• Experiment tracking and evaluation
• Cost optimization for inference workloads

Education & Experience

• Bachelors or Master’s degree in Computer Science, AI, Data Science, or related fields
• 4–10+ years of software / cloud engineering experience
• 2+ years hands-on experience in GenAI / LLM-based solutions preferred