data-pipelines jobs in San Diego

Lead Forward Deployed Engineer responsible for architecting and delivering LLM-enabled applications (copilots, agentic workflows, assistants) using enterprise AI platforms like Claude, Gemini, or GPT-4o. Drive end-to-end RAG pipeline design, prompt engineering, evaluation frameworks, and data foundations while mentoring junior engineers and enforcing delivery standards across multi-pod engagements.…See more Require 7+ years software/data engineering experience, 1+ year hands-on GenAI/LLM production deployment, and proven ability to lead project workstreams and translate business problems into AI solutions.

As L3 production support lead for ClaimBeacon, a Guidewire-integrated agentic AI platform, you'll own end-to-end troubleshooting and resolution of production bugs, data pipeline failures, AI agent issues, and small enhancements across the full stack (Java, Python, Flutter). You'll reproduce issues using logs and monitoring tools, decide what escalates to core engineering, and ensure fixes maintain data/AI governance and compliance standards.…See more The role requires 5+ years of software engineering or production support experience with proven end-to-end ownership of production issues, plus comfort working across frontend, backend, data, and infrastructure in a live, high-pressure environment.

Senior software engineer responsible for designing, developing, and maintaining cloud-based backend systems and data pipelines for medical device software at Dexcom. You'll build distributed systems, APIs, and ETL/ELT pipelines that ingest and transform large-scale data, integrate AI insights, and serve internal systems and external partners.…See more The role requires 5–8 years of experience with general-purpose programming (Java/Kotlin/Scala preferred), cloud platforms (AWS/Azure/GCP), CI/CD, and data platform architecture, with mentorship of junior engineers expected.

This role blends data science expertise with hands-on automation engineering to design and deploy intelligent process automation solutions across business units. You will build, train, and validate both classical ML and LLM-based models — including fine-tuning and RAG — and architect end-to-end systems connecting CRM, APIs, and AI services on AWS infrastructure.…See more Responsibilities span solution design, CI/CD implementation with Jenkins, Python development, agentic AI capabilities, and business translation, with emphasis on measurable business outcomes and compliance.

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.

The Cloud Optimization Engineer – FinOps will manage hybrid cloud spend across private and public cloud environments, using Flexera to monitor costs and deliver monthly reporting and dashboards to stakeholders. The role involves analyzing cloud architectures and workload patterns to identify cost drivers, building and automating data pipelines and cost models, and implementing cost optimization actions such as rightsizing and reservations.…See more You will collaborate hands-on with engineering teams, develop forecasting models, enforce tagging and governance standards, and drive FinOps best practices across the organization. The position requires 5+ years of FinOps and cloud financial management experience, proficiency in Azure or GCP, and advanced skills in Excel, SQL, Python, and PowerShell.

Join Amazon Private Brands' Selection Guidance team as a Data Scientist II to develop generative AI, machine learning, and statistical solutions for product assortment and business decisions. You will investigate business problems, invent novel solutions and prototypes, and guide other scientists on their designs and implementations.…See more Current research areas include named entity recognition, product substitutes, pricing optimization, agentic AI, and large language models. You'll partner with business stakeholders, perform data analysis and build pipelines, deploy production software, and publish research across Amazon.

Principal System Architect responsible for designing and maintaining a complex enterprise automation ecosystem spanning RPA, Machine Learning, and GenAI across multiple business units. The role requires architecting data pipelines integrating diverse systems (SAP, ERP, QMS, PLM tools like Windchill), leading automated workflow solutions (ECPs, QA defect prediction, EVMS reporting), and ensuring end-to-end cybersecurity compliance with DoD standards, CMMC, and Zero Trust architectures.…See more This is a senior technical leadership position requiring 14+ years of IT system architecture experience, deep expertise in intelligent automation and ML/AI, and the ability to mentor teams and present technical strategies to executive stakeholders.

A forward-deployed engineer who builds and deploys AI/GenAI-powered solutions on Databricks and cloud platforms for forensic discovery and financial crime clients. The role requires hands-on expertise in Databricks, cloud services (AWS/Azure/GCP), and production AI/LLM systems, combined with the ability to lead client engagements, translate business requirements into technical architectures, and troubleshoot integration issues across enterprise environments.…See more You will design reusable assets, document workflows, and enable client teams to sustain delivered solutions while navigating security, compliance, and privacy reviews.

Sr. Manager overseeing a data engineering team with responsibility for hiring, coaching, and performance management while remaining hands-on with architecture, design reviews, and technical decisions.…See more The role requires designing and operating production data pipelines and lakehouse platforms using AWS (S3, Glue, Athena, Lambda) and Snowflake, with hands-on proficiency in SQL and Python. Responsibilities include establishing data governance, compliance, security controls, CI/CD practices, and collaborating across engineering, analytics, product, and clinical teams to translate requirements into platform capabilities. The manager must balance delivery priorities, technical debt reduction, operational excellence, and emerging-technology evaluation while driving outcomes that affect broader business objectives.

Design and implement secure, scalable cloud infrastructure supporting enterprise data platforms, analytics workloads, and AI/ML environments. Lead cloud architecture strategy including service provider selection, deployment models, and migration techniques.…See more Evaluate emerging cloud and virtualization technologies, collaborate with data engineering and data science teams on enterprise data strategy alignment, and maintain expertise in cloud service delivery models, containers, automation, and infrastructure management.

Design, develop, and integrate Python-based software applications, data pipelines, REST APIs, and user interfaces for defense/mission-critical systems. Work directly with customers to translate operational requirements into technical solutions, support hardware integration and testing, and maintain comprehensive engineering documentation.…See more The role requires proficiency in Python, Git, Linux, and REST API design, with experience testing and debugging multi-component systems. Candidates should be comfortable presenting technical concepts to customers and collaborating within small engineering teams across the full software-development lifecycle.

Engineering Lead responsible for hands-on ML research, model development, and production AI system deployment in a live trading environment. You will work closely with quantitative research leadership to shape and execute the research agenda, design and train models, lead day-to-day technical execution across the engineering team, and mentor engineers to raise overall technical standards.…See more The role requires 4–6 years of applied ML/AI experience with 1–2 years of technical leadership, strong proficiency in Python and PyTorch, and a track record of shipping production ML systems where model performance directly impacts real-world outcomes.

Design, build, and operate production ML systems across the full lifecycle—from data preparation and model training through deployment, monitoring, and continuous improvement. Partner with data scientists, software engineers, and platform teams to turn research prototypes into reliable, scalable services.…See more Work across recommendation, ranking, forecasting, classification, NLP, and generative AI depending on product priorities. Balance model quality, inference latency, scalability, and operational resilience as equally important outcomes.

The Applied AI Engineer develops, deploys, and maintains generative AI solutions for insurance products and customer experiences. Responsibilities include implementing end-to-end AI/ML pipelines using AWS services (SageMaker, Lambda, ECS/EKS), feature engineering with Snowflake, model fine-tuning and testing, and monitoring production performance.…See more The role requires 3+ years of AI/ML experience with 1–2 years in generative AI or LLMs, hands-on cloud deployment experience, and proficiency in Python, PyTorch, TensorFlow, or Hugging Face Transformers.

Founding Product Engineer to build a greenfield Physical AI product for fleet safety and operations. The role spans full-stack development across cloud infrastructure (AWS, containers, CI/CD), multi-tenant backend systems with RBAC and SSO, real-time dashboards, compliance analytics, and AI-agentic features for alert triage and coaching workflows.…See more You'll own end-to-end problems from edge devices to cloud to user experience, shipping fast with direct customer feedback, and working across backend (Go, Python, Java), frontend (TypeScript/JavaScript), infrastructure, and AI integration. Requires 7+ years of software engineering with demonstrated range across multiple technical disciplines.

This contract role involves designing and maintaining clinical data pipelines that ingest, transform, and validate healthcare datasets while ensuring HIPAA compliance and data quality. The engineer will collaborate with clinical and analytics teams to understand data requirements, implement quality checks, manage data lineage for audit purposes, and prepare optimized datasets for reporting and statistical analysis.…See more The position requires 5–10 years of data engineering or clinical data management experience, strong SQL proficiency, expertise in data quality validation, and familiarity with regulated healthcare environments.

Engineering Lead responsible for designing, developing, and deploying production ML models for live trading environments while leading day-to-day technical execution across the engineering team. You will work with leadership to shape the research agenda, mentor engineers, drive adoption of AI tools to accelerate engineering workflows, and build/improve core ML infrastructure including data pipelines, training workflows, and inference systems.…See more The role requires 4–6 years of applied ML/AI experience with 1–2 years leading technical projects or teams, strong proficiency in Python and PyTorch, and a track record of shipping ML systems where model performance directly impacts business outcomes.

Lead a data engineering team responsible for building scalable data pipelines, trusted datasets, and reusable data products powering analytics, experimentation, and AI across Scribd. This role blends technical leadership with hands-on architecture guidance; you'll establish engineering standards, drive design reviews, mentor engineers, and partner cross-functionally to translate business needs into production-grade data solutions.…See more Requires 10+ years in data engineering or data platforms, 3+ years leading engineering teams, deep expertise in dimensional modeling and scalable data architectures, and strong SQL plus Python/Scala skills. You'll work with modern cloud data platforms like Databricks, Snowflake, and BigQuery, and distributed processing frameworks like Spark.

SAIC seeks a System Administrator for expeditionary systems integration, focusing on AI capabilities across sensor kits, unmanned systems, command-and-control, and TAK platforms. The role involves hands-on troubleshooting of network connectivity and OS updates on Linux edge servers, testing machine learning capabilities in field experiments, and collaborating with ML engineers and data engineers to integrate and monitor tactical data pipelines.…See more Responsibilities include building tactical systems for exercises, ensuring performant data flow between sensors and C2 systems, and working with software engineers to connect data infrastructure to operational systems. Required: 2+ years in system administration, data engineering, ML engineering, or related roles; intermediate Linux command-line proficiency; experience with edge servers, containerization (Docker/Kubernetes), and tactical system troubleshooting.

Senior software engineer focused on building cloud-based backend services and data platforms for medical device systems at Dexcom. Design and develop end-to-end data pipelines supporting large-scale ingestion, transformation, and storage, while building and maintaining APIs that expose data to internal systems, partners, and mobile apps.…See more Collaborate with Data Engineers, AI/ML Engineers, and Platform teams to integrate AI insights into production systems, ensuring high standards for reliability, performance, scalability, and data quality. Lead through code reviews, design mentorship, and technical guidance while contributing to architecture decisions and long-term platform roadmap.

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 and optimize a high-throughput compute stack for reliable, production-grade data ingestion, processing, storage, and delivery. The role focuses on making stateful, multi-threaded pipelines fast and observable under real-world constraints—profiling bottlenecks, hardening recovery behavior, and productizing machine-learning models (neural networks, tree-based, unsupervised) to meet performance, reliability, and quality standards.…See more You will work alongside algorithm and infrastructure engineers, using measurements to drive performance improvements and ensure production interfaces remain durable as the system evolves. Requires strong systems-level experience with concurrency, I/O, and production debugging in C++, Rust, CUDA, or C; a track record of shipping production software; and hands-on expertise in resilient data pipelines and GPU computing.

A junior data engineer will contribute to data pipeline development across AWS, Azure, Salesforce, and MuleSoft, learning the full engineering lifecycle with guidance. Core responsibilities include building and maintaining data pipelines, implementing Kimball-based data models, writing Python orchestration and data-quality scripts, and supporting Power BI analytics.…See more The role emphasizes AI-assisted development using Claude Code, Cursor, and GitHub Copilot, with requirements for 0–2 years of experience, 6+ months of Python, intermediate SQL, at least one end-to-end pipeline project, and Power BI/Tableau dashboard experience.