AWS Cloud Engineer

Role: AWS Cloud Engineer Work Mode: Remote Long Term Contract Rate: Negotiable TECHNICAL SKILLS Must Have • AWS services- Bedrock, SageMaker, ECS and Lambda • Demonstrated contributions to open-source AI/ML/Cloud projects • Demonstrated proficiency in Python and Golang coding languages • Experience implementing RAG architectures and using frameworks and ML tooling like: Transformers, PyTorch, TensorFlow, and LangChain • LLM • Ph.D. in AI/ML/Data Science Nice To Have • Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config) • FinOps DUTIES AND RESPONSIBILITIES: • Design, develop, and maintain modular AI services on AWS using Lambda, SageMaker, Bedrock, S3, and related components—built for scale, governance, and cost-efficiency. • Lead 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. • Design and 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. • Collaborate with internal stakeholders to understand business goals and translate them into secure, scalable AI systems. • Own 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: • 10+ years of proven software engineering experience with a strong focus on Python and GoLang and/or Node.js. • Demonstrated contributions to open-source AI/ML/Cloud projects, with either merged pull requests or public repos showing real usage (forks, stars, or clones). • Direct, hands-on development of RAG, semantic search, or LLM-augmented applications, using frameworks and ML tooling like Transformers, PyTorch, TensorFlow, and LangChain—not just experimentation in a notebook. • Ph.D. in AI/ML/Data Science and/or named inventor on pending or granted patents in machine learning or artificial intelligence. • 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 success delivering production-ready software with release pipeline integration. NICE-TO-HAVES: • Policy as Code development (i.e., Terraform Sentinel) to manage and automate cloud policies, ensuring compliance • Experience optimizing cost-performance in AI systems (FinOps mindset). • Awareness of data privacy and compliance best practices (e.g., PII handling, secure model deployment). • Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config).