AI/ML Engineer

Benefits:

• Bonus based on performance
• Dental insurance
• Health insurance
• Vision insurance Qualifications
• Bachelor’s Degree
• 6+ years cloud architecture experience
• 3+ years building production GenAI/LLM systems on AWS.
• Strong Python and AWS expertise, including Lambda, ECS/EKS, S3, SageMaker, Docker and Kubernetes.
• Production experience with vector databases and designing ingestion + embedding pipelines for both batch and streaming workloads.
• Hands-on with prompt design, evaluation, LLM orchestration, and RAG implementation patterns.
• Experience deploying and operating model- serving or MCP – like server infrastructure (selfhosted or managed).
• Proficient with IaC and delivery tooling, including Terraform/CloudFormation, GitOps, and CI pipelines.
• Experience with model-serving infrastructure, such as Amazon SageMaker, NVIDIA Triton, Ray Serve, or similar platforms.
• Hands-on experience with GenAI libraries and frameworks, including LangChain, LlamaIndex, Hugging Face, and OpenAI APIs.
• Deep operational expertise with vector databases, such as Pinecone, Milvus, Weaviate, or Qdrant.
• AWS Solutions Architect, AWS DevOps Engineer, or equivalent industry certifications.

Responsibilities

• Cloud Architecture & Infrastructure, Design scalable, secure AWS architectures
• LLM & GenAI Platforms, Lead integration of API-based and self-hosted LLMs, implement RAG solutions
• Prompting & Evaluation, Develop prompt engineering strategies, reusable templates, and evaluation frameworks
• Vector Databases & Retrieval Pipelines, Implement and maintain vector stores (OpenSearch, Pinecone, Milvus, Qdrant)
• Data Ingestion & Processing Pipelines
• Microservices & Serverless Systems
• Python Development & AI Tooling
• Security, Governance & Cross-Functional Leadership