AWS Cloud Engineer III

Job role: AWS Cloud Engineer III

Location: Remote

Need w2 consultants and must have banking experience

Duration:

Lead AI AWS Engineer who has actually built AI/ML applications in cloud not just read about them. This role centers on hands-on development of retrieval-augmented generation (RAG) systems, fine-tuning LLMs, and AWS-native microservices that drive automation, insight, and governance in an enterprise environment. You ll design and deliver scalable, secure services that bring large language models into real operational use connecting them to live infrastructure data, internal documentation, and system telemetry.

You ll be part of a high-impact team pushing the boundaries of cloud-native AI in a real-world enterprise setting. This is not a prompt-engineering sandbox or a resume keyword trap. If you ve merely dabbled in SageMaker, mentioned RAG on LinkedIn, or read about vector search this isn t the right fit. We re looking for candidates who have architected, developed, and supported AI/ML services in production environments.

This is a builder s role within our Public Cloud AWS Engineering team. We aren t hiring buzzword lists or conference attendees. If you ve built something you re proud of especially if it involved real infrastructure, real data, and real users we d love to talk. If you re still learning, that s great too but this isn t an entry-level role or a theory-only position.

Duties & Responsibilities:

• Develop and maintain modular AI services on AWS using Lambda, SageMaker, Bedrock, S3, and related components built for scale, governance, and cost-efficiency.
• Contribute to the end-to-end development of RAG pipelines that connect internal datasets (e.g., logs, S3 docs, structured records) to inference endpoints using vector embeddings.
• Fine-tune LLM-based applications, including Retrieval-Augmented Generation (RAG) using LangChain and other frameworks.
• Tune retrieval performance using semantic search techniques, proper metadata handling, and prompt injection patterns.
• Work within the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code (Terraform).
• Support the development and evolution of reusable platform components for AI/ML operations.
• Create and maintain technical documentation for the team to reference and share with our internal customers.
• Excellent verbal and written communication skills in English.

Required Knowledge, Skills and Abilities:

• 7-10 years of proven software engineering experience with a strong focus on Python and GoLang.
• Must have a strong background in document tokenization, embeddings, various word models (such as Word2Vec, FastText, TF-IDF, BERT, GPT, ELMo, LDA, Transformers), and experience with NLP pipelines.
• Direct, hands-on development of RAG, semantic search, or LLM-augmented applications, and using frameworks and ML tooling like Transformers, PyTorch, TensorFlow, and LangChain not just experimentation in a notebook.
• Deep expertise with AWS services, especially Bedrock, SageMaker, ECS, and Lambda.
• Proven experience fine-tuning large language models, building datasets, and deploying ML models to production.
• Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config).
• Demonstrated experience in Infrastructure as Code best practices and experience with building Terraform modules for AWS cloud.
• Strong background in Git-based version control, code reviews, and DevOps workflows.
• Demonstrated success delivering production-ready software with release pipeline integration.

Must Have Skills:

• AWS Services - Bedrock, SageMaker, ECS and Lambda
• Demonstrated Proficiency in Python and Golang Coding Languages
• LLM
• Natural Language Processing (NLP)
• Retrieval Augmented Generation

Nice to Have Skills:

• AWS or relevant cloud certifications.
• Policy as Code development (i.e., Terraform Sentinel).
• Experience optimizing cost-performance in AI systems (FinOps mindset).
• Data science background or experience working with structured/unstructured data.
• Awareness of data privacy and compliance best practices (e.g., PII handling, secure model deployment).
• Experience with Node.js.
• Exposure to Fin Ops and Cloud Cost Optimization
• Node.JS
• Policy as Code Development (I.e Terraform Sentinel)