opensearch jobs in San Diego

Build production AI features and workflows powered by large language models, including retrieval-augmented generation (RAG), agentic orchestration, and tool/function calling integrations. You will design and implement scalable microservices in Python or Java that connect foundation model APIs to business data and internal systems, with responsibility for AI quality, evaluation, observability, and safe deployment on AWS.…See more Collaborate with cross-functional teams to prototype solutions, measure outcomes, and move proven capabilities into production while maintaining reliability, security, and auditability.

Design, develop, and optimize machine learning systems for production AI platforms, focusing on model development, inference optimization, and scalable ML infrastructure. Responsibilities include building ML pipelines and frameworks, optimizing model inference for latency and cost, integrating LLMs into APIs and microservices, and designing data pipelines for preprocessing and feature engineering.…See more The role requires strong software engineering fundamentals combined with deep ML expertise, working across PyTorch/TensorFlow, model-serving systems, distributed computing, and GPU environments.

Design, develop, and optimize production machine learning systems including model development, inference optimization, and scalable ML infrastructure. Build end-to-end ML pipelines from training through deployment, optimize model inference for latency and cost, integrate LLM/ML models into APIs and microservices, and engineer data pipelines for preprocessing and validation.…See more Requires strong software engineering fundamentals with deep ML expertise, proficiency in Python and systems languages, and experience with ML frameworks, model serving, and distributed computing.

Senior technical leader responsible for integrating AI/ML systems into secure, scalable DoD-compliant environments. Will architect AI system deployments, establish DevSecOps practices, ensure compliance with RMF/NIST 800-53/DISA STIG standards, and provide technical guidance across engineering teams.…See more Requires deep expertise in AI/ML production systems, containerization, CI/CD, and cybersecurity controls, with hands-on experience in Python, Kubernetes, and modern infrastructure tooling.

Site Reliability Engineer responsible for monitoring system health, detecting issues before they escalate, and owning incident response and debugging end-to-end. You'll build logging and observability tooling, automate deployment pipelines, manage capacity planning, and drive platform reliability at scale.…See more The role requires 5+ years in SRE/DevOps, hands-on production incident response, and strong communication during live troubleshooting. You'll work with AWS, Terraform, Datadog, Python/Bash scripting, and modern infrastructure-as-code practices.

Design, build, and maintain secure enterprise data platforms supporting AI/ML workloads, with responsibility for database optimization, CI/CD automation, and DoD compliance. Mentor junior engineers and provide technical leadership on architecture decisions.…See more Implement security controls aligned with NIST 800-53, RMF, and DISA STIG standards throughout the platform lifecycle. Develop ETL/ELT pipelines, support data lakes and lakehouses, and evaluate emerging data technologies for mission fit.

Principal AI Platform Engineer leading enterprise AI infrastructure strategy and governance at the platform boundary. Responsible for implementing and maintaining Bedrock Guardrails and Microsoft Purview DLP controls, managing AI vendor relationships with Microsoft and AWS, producing governance artifacts and runbooks, and leading internal AI adoption through community building and enablement.…See more Requires 7–10 years of cloud/DevOps experience with hands-on production expertise in AWS Bedrock (Agents, Knowledge Bases, Guardrails) and Microsoft 365 Copilot deployment in enterprise environments, including data integration, RAG architecture, and AI security controls.

Build and test agent behavior for an AI-driven service, including test case design and evaluation checks distinct from traditional software testing. Support data ingestion pipelines using AWS and BigData tools (PySpark, Airflow), collaborate with product and senior engineers to ship features, and contribute to production support (monitoring, alerting, incident response).…See more Requires 1–2 years of software engineering experience, a Master's in Computer Science or related field, proficiency in Python or another backend language, SQL/NoSQL fundamentals, basic REST API design, and hands-on experience with at least one LLM project (tool calling, chaining, retrieval, or multi-step coordination). Self-directed learner excited about AI tooling and focused on shipping end-to-end products.

Build production AI features and services using foundation model APIs, retrieval-augmented generation (RAG), and agentic workflows. You will implement scalable microservices in Python or Java, develop evaluation and quality frameworks for AI systems, and collaborate across teams to integrate AI capabilities into enterprise workflows.…See more The role requires hands-on applied AI experience, strong backend or platform engineering skills, and familiarity with vector databases, LLM orchestration tools, and cloud infrastructure.

Lead the design, deployment, and governance of an enterprise AI platform integrating AWS Bedrock, Microsoft 365 Copilot, and structured/unstructured data sources. Act as the primary technical authority for AI infrastructure, RAG architecture, security controls, and AI adoption across business units, with responsibility for platform budget, vendor contracts, and internal capability building through mentoring and community-of-practice leadership.…See more Requires hands-on production experience with Bedrock (Agents, Knowledge Bases, Guardrails), M365 Copilot enterprise deployment, and proven ability to operate as a solo platform owner in a matrixed organization without direct reports.

Senior Forward Deployed Engineer responsible for building and deploying GenAI/LLM-powered solutions on AWS, working directly with enterprise clients to translate business needs into production AI systems. The role requires 5+ years of software/data engineering experience, 1+ years hands-on with AWS AI&Data services (Bedrock, Neptune, OpenSearch), and the ability to lead technical workstreams while mentoring team members.…See more You will prototype solutions, develop scalable AI patterns with human-in-the-loop controls, write production-quality code with strong testing and CI/CD practices, and create reusable assets including code libraries and reference implementations. 50% travel required.

Forward Deployed Engineer who partners with enterprise clients to identify business needs and prototype production-grade GenAI/LLM solutions on AWS. Designs and builds AI-enabled agentic platforms, workflows, and scalable engineering patterns using AWS AI&Data services (Bedrock, Neptune, OpenSearch).…See more Delivers production-quality code with strong testing, CI/CD, logging, and documentation practices while mentoring team members and translating complex business problems into technical AI solutions.

Design, develop, and optimize machine learning systems and models for production AI platforms, with focus on inference optimization, scalable ML infrastructure, and deployment. Responsibilities include building ML pipelines, optimizing model inference across hardware environments, integrating LLMs and models into APIs and microservices, designing data pipelines for ingestion and feature engineering, and collaborating cross-functionally on end-to-end ML solutions.…See more Requires strong software engineering fundamentals combined with deep ML expertise, proficiency in Python and at least one systems language (C++, Rust, or Go), and solid understanding of ML frameworks, transformer architectures, and model deployment systems.

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.…See more 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.