Gen AI Engineer – AWS
Excellence and Eminence LLP<\/div>
<\/div>
<\/div>
<\/div>
<\/div>
We are seeking a highly skilled Gen AI Engineer – AWS<\/b> to architect, build, and optimize next -generation GenAI -powered applications<\/b> leveraging Large Language Models (LLMs)<\/b> and Retrieval -Augmented Generation (RAG)<\/b> pipelines. The ideal candidate will combine strong software engineering and cloud architecture expertise with a deep understanding of AI/ML systems to deliver scalable, secure, and high -performing GenAI solutions for enterprise use cases, including technical documentation, maintenance, and support workflows<\/b>.
<\/p>
This role involves designing advanced RAG architectures, fine -tuning LLMs, implementing microservices, and maintaining continuous evaluation frameworks — all while ensuring enterprise -grade compliance, observability, and operational efficiency on AWS infrastructure.
<\/p>
<\/span><\/div>
<\/span><\/div>
<\/span>
<\/h2>Architect and build<\/b> GenAI -powered applications using LLMs<\/b> and advanced RAG pipelines<\/b> for technical documentation, maintenance, and enterprise support workflows.
<\/p><\/li>Design and implement<\/b> retrieval -augmented generation (RAG) systems leveraging vector databases<\/b> and semantic search<\/b> to enhance contextual information retrieval.
<\/p><\/li>Integrate and fine -tune<\/b> LLMs (OpenAI, Anthropic, Amazon Bedrock, HuggingFace, etc.) for domain -specific applications and continuous improvement cycles.
<\/p><\/li>Develop and maintain<\/b> evaluation frameworks for LLM outputs — assessing relevancy, faithfulness, summarization quality, and contextual accuracy<\/b>.
<\/p><\/li>Build and deploy<\/b> scalable and secure microservices and APIs<\/b> (REST/GraphQL) using FastAPI/Flask<\/b> for GenAI workflows.
<\/p><\/li>Collaborate<\/b> cross -functionally with product, domain, and platform teams to define requirements and deliver innovative, production -ready GenAI solutions.
<\/p><\/li>Implement monitoring and observability<\/b> using tools like Prometheus, MLflow, CloudWatch, and DataDog<\/b> to ensure model reliability and quality assurance.
<\/p><\/li>Ensure security and compliance<\/b> through best practices in IAM, KMS, VPC endpoints, encryption, and audit trail management.
<\/p><\/li>Leverage AWS DevOps practices<\/b> (CodePipeline, CDK, Terraform, CloudFormation) for automated deployment, scaling, and lifecycle management of GenAI services.
<\/p><\/li>Stay up to date with emerging GenAI frameworks, architectures, and tools<\/b> to continually enhance solution quality and performance.
<\/p><\/li>Requirements<\/span>
<\/h2>Bachelor’s or Master’s degree<\/b> in Computer Science, Artificial Intelligence, Machine Learning, or related field.
<\/p><\/li>5+ years<\/b> of hands -on experience in AI/ML application development, with at least 2+ years<\/b> focused on GenAI and LLM systems<\/b>.
<\/p><\/li>Proven expertise in GenAI frameworks<\/b> — LangChain, HuggingFace, Amazon Bedrock, OpenAI, and Anthropic models.
<\/p><\/li>Experience designing and deploying RAG architectures<\/b> using vector databases<\/b> such as Pinecone, FAISS, or Milvus.
<\/p><\/li>Strong skills in Python<\/b>, API development (FastAPI/Flask)<\/b>, and containerization (Docker, Kubernetes)<\/b>.
<\/p><\/li>Practical knowledge of AWS cloud services<\/b>, security<\/b>, and DevOps pipelines<\/b> (Terraform, CDK, CloudFormation).
<\/p><\/li>Experience with monitoring and evaluation tools<\/b> — G -Eval, Prometheus, MLflow, or similar.
<\/p><\/li>Familiarity with multi -modal retrieval systems<\/b> integrating text, image, and telemetry data.
<\/p><\/li>Prior experience in regulated domains<\/b> (aviation, manufacturing, healthcare) with compliance and audit requirements is a plus.
<\/p><\/li>Exposure to MLOps practices<\/b> — model deployment, rollback, versioning, and lifecycle management.
<\/p><\/li>Strong problem -solving skills, architectural thinking, and ability to translate research into production -ready systems.
<\/p><\/li>
<\/span>
<\/h2>
<\/div><\/span>
Architect and build<\/b> GenAI -powered applications using LLMs<\/b> and advanced RAG pipelines<\/b> for technical documentation, maintenance, and enterprise support workflows. Design and implement<\/b> retrieval -augmented generation (RAG) systems leveraging vector databases<\/b> and semantic search<\/b> to enhance contextual information retrieval. Integrate and fine -tune<\/b> LLMs (OpenAI, Anthropic, Amazon Bedrock, HuggingFace, etc.) for domain -specific applications and continuous improvement cycles. Develop and maintain<\/b> evaluation frameworks for LLM outputs — assessing relevancy, faithfulness, summarization quality, and contextual accuracy<\/b>. Build and deploy<\/b> scalable and secure microservices and APIs<\/b> (REST/GraphQL) using FastAPI/Flask<\/b> for GenAI workflows. Collaborate<\/b> cross -functionally with product, domain, and platform teams to define requirements and deliver innovative, production -ready GenAI solutions. Implement monitoring and observability<\/b> using tools like Prometheus, MLflow, CloudWatch, and DataDog<\/b> to ensure model reliability and quality assurance. Ensure security and compliance<\/b> through best practices in IAM, KMS, VPC endpoints, encryption, and audit trail management. Leverage AWS DevOps practices<\/b> (CodePipeline, CDK, Terraform, CloudFormation) for automated deployment, scaling, and lifecycle management of GenAI services. Stay up to date with emerging GenAI frameworks, architectures, and tools<\/b> to continually enhance solution quality and performance. Bachelor’s or Master’s degree<\/b> in Computer Science, Artificial Intelligence, Machine Learning, or related field. 5+ years<\/b> of hands -on experience in AI/ML application development, with at least 2+ years<\/b> focused on GenAI and LLM systems<\/b>. Proven expertise in GenAI frameworks<\/b> — LangChain, HuggingFace, Amazon Bedrock, OpenAI, and Anthropic models. Experience designing and deploying RAG architectures<\/b> using vector databases<\/b> such as Pinecone, FAISS, or Milvus. Strong skills in Python<\/b>, API development (FastAPI/Flask)<\/b>, and containerization (Docker, Kubernetes)<\/b>. Practical knowledge of AWS cloud services<\/b>, security<\/b>, and DevOps pipelines<\/b> (Terraform, CDK, CloudFormation). Experience with monitoring and evaluation tools<\/b> — G -Eval, Prometheus, MLflow, or similar. Familiarity with multi -modal retrieval systems<\/b> integrating text, image, and telemetry data. Prior experience in regulated domains<\/b> (aviation, manufacturing, healthcare) with compliance and audit requirements is a plus. Exposure to MLOps practices<\/b> — model deployment, rollback, versioning, and lifecycle management. Strong problem -solving skills, architectural thinking, and ability to translate research into production -ready systems.
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>Requirements<\/span>
<\/h2>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li>
<\/p><\/li><\/span>
<\/h2>
<\/div><\/span>