Software Gigs

Design, develop, and optimize machine learning systems for production AI platforms, including model development, inference optimization, and scalable ML infrastructure. Build training-to-deployment pipelines, optimize model serving for latency and cost, and integrate LLMs and generative AI models into microservices and APIs. Develop data pipelines for ingestion, preprocessing, and feature engineering while collaborating cross-functionally with product, platform, and hardware teams to deliver end-to-end ML solutions.

The AI Architect will lead the design, development, and deployment of enterprise-scale generative AI solutions across Azure and AWS, partnering with business and engineering leaders to define AI strategy and architecture standards. The role requires deep expertise in modern LLMs (Claude, Gemini, OpenAI), multi-cloud AI platforms, and enterprise architecture, with 10+ years in software/cloud architecture and 5+ years architecting AI/ML solutions. Key responsibilities include designing end-to-end GenAI solutions leveraging RAG, agentic AI, and multi-agent systems; establishing cloud-native deployment patterns; and defining governance, compliance, and responsible AI frameworks. The ideal candidate will serve as a trusted technical advisor to executives, mentor engineering teams, and drive AI adoption through reusable frameworks and reference architectures.

Design, build, and ship LLM-powered capabilities end to end—from prototyping and fine-tuning models to deploying production agents and retrieval systems. Own the full stack: prompt and context engineering, multi-step agent design with tool calling, RAG systems (embeddings, chunking, hybrid search, reranking), fine-tuning on multi-GPU with LoRA/QLoRA, evaluation and tracing infrastructure, and clean APIs for other engineers. Work in a secure, distributed environment where the platform runs on customer compute, cloud, or hybrid setups, requiring expertise with both commercial and self-hosted models.

Build the foundational agentic AI layer for a materials-science platform, including multi-model provider abstraction, agent orchestration with stateful checkpoints, retrieval systems, prompt versioning, and comprehensive tracing and evaluation frameworks. You'll design agents that plan and reason over tool calls in production, implement human-in-the-loop safety gates, and ensure all LLM behavior remains auditable and cost-tracked across customers' secure environments. The role demands deep production experience with agentic and LLM systems: async Python, structured outputs, memory and context management, multi-step workflow orchestration, and evaluation harnesses that catch regressions before deployment.

Junior AI Software Developer supporting Navy cybersecurity and mission operations in San Diego. You will participate in problem discovery with mission stakeholders, contribute to technical solution design, and deliver hands-on implementation of scripts, APIs, automation, and AI/LLM-enabled workflows. Work involves integrating systems via REST/HTTP APIs, writing and testing Python code, using version control, and documenting technical solutions for both technical and non-technical audiences while adhering to RMF, security controls, and responsible AI practices in classified or controlled environments.

Senior Applied AI Engineer at West Health will design, build, and deploy production-quality AI-powered tools and workflows using large language models, multi-agent architectures, and orchestration platforms. The role involves building intelligent automation pipelines with tools like n8n, LangChain/LangGraph, and Microsoft Copilot Studio, integrating AI capabilities with existing data infrastructure (Snowflake, Python), and conducting data analysis to identify high-impact AI opportunities. The ideal candidate combines deep practical expertise in applied AI with the ability to rapidly move from concept to fully working, well-documented applications that create measurable value for healthcare delivery and senior care initiatives.

Lead end-to-end development of AI agents and autonomous engineering tools for Teradata's enterprise platform, managing multiple global engineering teams. Shape engineering strategy for agent platforms, LLM gateways, vector stores, observability frameworks, and low-code/no-code/pro-code agent frameworks. Drive architecture standardization, governance practices, operationalization pipelines, and ROI metrics while partnering with product, architecture, and AI platform teams. Requires 12+ years software engineering experience with 5+ years in technical leadership, hands-on expertise with LLMs, agent frameworks, distributed systems, cloud platforms (AWS, Azure, GCP), and modern CI/CD tools.

Staff Software Developer who will design, develop, and maintain AI-enabled knowledge management and decision-support platforms for environmental and engineering applications. Responsibilities include building data ingestion workflows, implementing retrieval-augmented generation (RAG) systems with vector embeddings and semantic search, developing backend services and APIs, creating secure web-based UIs, and working with cloud services (Azure AI Search, Blob Storage, PostgreSQL). The role requires strong software development skills demonstrated through a public code portfolio, familiarity with LLMs and prompt engineering, and the ability to collaborate across technical teams to translate project needs into practical features.