mlops 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.

Join XiFin's Advanced Data Analytics team as an Associate Machine Learning Engineer to design, develop, and deploy machine learning models with a focus on LLMs, Generative AI, and Agentic AI in healthcare and revenue cycle domains. You will contribute to MLOps pipelines, perform data preparation and prompt engineering, collaborate with cross-functional teams, and validate models for production use.…See more The role requires a Bachelor's degree in Computer Science, Data Science, or related field, with proficiency in Python, ML libraries (scikit-learn, TensorFlow, PyTorch), and foundational knowledge of GenAI architectures, vector databases, and LLM orchestration frameworks.

As an Autonomy Engineer focused on VLA (Vision-Language-Action) pre-training, you will develop and train deep learning policies for humanoid robots, owning the full pipeline from data collection and curation to model deployment. You'll pre-train base models on diverse multi-embodiment trajectory corpora, fine-tune policies for specific tasks, work with MLOps teams to scale distributed training, and build continuous pipelines that ingest synthetic data and teleop logs.…See more This is primarily a deep learning role requiring 3+ years of experience shipping neural network models, hands-on expertise with LLMs, VLMs, or generative models, and strong proficiency in Python and PyTorch or JAX.

Forward Deployed Engineer who embeds with clients to identify business needs and translate high-value GenAI use cases into production solutions. Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms, delivering production-quality code with strong testing, CI/CD, logging, and documentation practices.…See more Lead working sessions, prototype solutions, and mentor team members while balancing architecture decisions around quality, safety, latency, cost, and model risk. Requires 3+ years software/data engineering experience, 1+ years hands-on GenAI/LLM deployment, and 1+ years with Microsoft AI & Data (Azure AI Foundry).

Senior forward-deployed engineer at Deloitte GPS responsible for developing scalable AI engineering patterns and production-quality GenAI/LLM solutions for government clients. Must have 7+ years software/data engineering experience, 1+ years hands-on with Palantir (Foundry, AIP, or Maven), and 1+ years building and deploying LLM-powered solutions in production.…See more Role emphasizes architecture balancing quality, safety, latency, cost, and model risk; delivering well-tested, documented code with strong CI/CD practices; and translating business problems into AI solutions while leading technical workstreams.

A forward-deployed engineer who embeds with enterprise clients to identify business needs and build production-grade AI solutions using Palantir platforms (Foundry, AIP, Maven). The role combines solution engineering—developing agentic AI systems, LLM-powered workflows, and scalable patterns with strong testing and CI/CD practices—with client engagement and stakeholder alignment.…See more Requires 3+ years of software/data engineering experience, 1+ years of hands-on GenAI/LLM deployment, and 1+ years working with Palantir; involves 50% travel and mentoring of junior team members.

Lead the scientific vision and execution of a generative AI platform for protein foundation models, starting with antibody and VHH design. You will build and manage a team of AI researchers and ML infrastructure engineers, define pretraining and post-training strategies across self-supervised learning, reinforcement learning, and transfer learning, and design confidence heads with out-of-distribution detection.…See more This is a founding role requiring deep expertise in structure-based generative modeling, multi-node GPU training, and the ability to shape cutting-edge computational approaches for therapeutic molecule discovery.

Senior AI/ML Engineer responsible for shipping end-to-end production ML systems that enable autonomous medical coding in a SaaS platform. Will design robust ML architectures with low-latency inference, build reliable data and model pipelines using modern MLOps practices (CI/CD, model registries, experiment tracking), establish evaluation strategies with statistical rigor, and implement model observability and governance including drift detection and bias assessments.…See more Requires 5+ years of industry experience building and operating ML systems in production, 3+ years with C# or Python, 3+ years on Azure, and 1+ years of LLMOps expertise including prompt engineering and RAG.

As an ML engineer at Booz Allen Hamilton, you'll design, build, and deploy production-grade AI/ML systems for Defense and Intelligence clients. You'll work across the full ML lifecycle—from model development in deep learning, computer vision, NLP, and signal processing through MLOps, containerization, and cloud deployment.…See more The role requires 2+ years of ML engineering or data science experience, proficiency with frameworks like TensorFlow and PyTorch, and the ability to obtain a Secret clearance.

Build and ship production ML systems end-to-end for autonomous medical coding in a SaaS platform, from problem framing through deployment and monitoring. Own the full lifecycle—data discovery, feature engineering, training, evaluation, and iteration—working with LLMs, deep learning, and modern MLOps tooling to handle millions of medical charts daily.…See more Design robust, low-latency architectures, establish rigorous evaluation strategies, and implement model observability, drift detection, and bias assessments. Collaborate across product, data, and platform teams to translate business goals into measurable ML outcomes.

Lead end-to-end AI platform architecture and infrastructure at enterprise scale, designing and implementing generative AI capabilities including LLMs, RAG systems, and agentic AI with a focus on model serving, inference optimization, and production deployment. Establish MLOps/LLMOps best practices, build automated CI/CD pipelines tailored for AI/ML applications, and architect vector database integration and cloud AI/ML services.…See more Mentor engineering teams, drive cross-functional collaboration between software, product, and data teams, and continuously evaluate and optimize AI platform performance, scalability, and reliability.

Senior engineer responsible for designing, building, and optimizing large-scale AI platforms for wireless, 5G, and connected devices. You will architect end-to-end AI/ML pipelines, integrate models into chipsets and cloud/edge environments, and deliver scalable inference services, tools, and SDKs.…See more Key responsibilities include model deployment, performance tuning on heterogeneous hardware, MLOps automation, and monitoring across silicon, software, and product teams.

Staff Engineer responsible for designing and building scalable test automation frameworks and CI/CD infrastructure for machine-learning systems at scale. The role requires expertise validating ML quality across the full lifecycle (data, training, evaluation, packaging, deployment, monitoring), GPU-accelerated workloads in Kubernetes, and integrated hardware-software systems.…See more You will develop automated evaluation suites, performance baselines, release thresholds, and observability solutions while mentoring teams on ML testing best practices and dependency/reproducibility standards.

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.

Develop, optimize, and validate AI/ML solutions that leverage Qualcomm's AI inference accelerators for data center and hybrid applications. Design and implement GenAI and LLM applications, perform model benchmarking, optimize deployment strategies, and drive system-level architecture decisions.…See more Apply deep expertise in ML frameworks, system performance profiling, and parallel computing to ensure best-in-class inference performance, power efficiency, and scalability across heterogeneous hardware.

As a Machine Learning Engineer at Qualcomm, you will design, develop, and deploy AI and Generative AI solutions for the Nimbus product line, focusing on network optimization, Digital Twin technologies, and LLM-based applications. You will collaborate with cross-functional engineering, product, and domain teams to translate business requirements into production-ready AI-enabled solutions, staying current with emerging AI technologies and best practices.…See more The role requires hands-on experience with model development, evaluation, deployment, and operationalization, along with strong software engineering practices for scalable and maintainable systems. You must communicate technical concepts effectively to both technical and non-technical audiences.

Staff Engineer or Architect role focused on building and orchestrating AI agents in a compliance-heavy, globally regulated environment. You will troubleshoot novel failures across models, tools, and infrastructure; set technical vision; and leverage LLMs, RAG, tool use, and agent frameworks (LangGraph, CrewAI, AutoGen) in production systems.…See more Requires 8+ years of software engineering experience, hands-on LLM and agent-framework work, end-to-end ownership, and demonstrated ability to use AI agents effectively in your own workflow while maintaining accountability and clear mental models of system behavior.

Qualcomm seeks a Machine Learning Engineer to design, develop, and deploy generative AI solutions focused on Large Language Models, Retrieval-Augmented Generation (RAG), and intelligent agent systems. The role involves implementing RAG-based solutions, optimizing LLM fine-tuning strategies, building evaluation frameworks, and maintaining agentic workflows for autonomous AI systems.…See more The candidate must have strong Python programming skills, practical LLM deployment experience, and familiarity with ML frameworks like PyTorch and TensorFlow, alongside knowledge of vector databases, cloud platforms (AWS, GCP, Azure), and modern MLOps infrastructure.

Build and deploy production-grade AI models that power Petco's e-commerce platform, including personalized recommendations, demand forecasting, and price optimization. Design and maintain robust MLOps pipelines for automated model retraining, monitoring, and 24/7 reliability across web and mobile.…See more Partner with Software Engineering and Mobile teams to integrate AI features into production systems, and mentor junior engineers on ML engineering best practices. Experiment with emerging technologies like Generative AI and LLMs to drive business impact.

Design, build, and operate enterprise-scale Databricks lakehouse platforms for a global semiconductor company. Lead AI-native development practices, machine learning enablement, and AIOps automation while mentoring engineering teams and establishing technical standards.…See more Drive platform modernization, cloud-native practices, and intelligent data/AI infrastructure at scale. Requires 4–5 years of hands-on Databricks experience, strong Python/SQL/Spark skills, MLOps expertise, and proven technical leadership.

Design, create, and implement production-grade AI/ML systems for Defense and Intelligence clients. You'll architect scalable machine learning solutions handling fast-moving data, deploy models across multiple data modalities using frameworks like TensorFlow and PyTorch, and integrate solutions into mission environments.…See more The role requires 2+ years of ML engineering or data science experience, proficiency with ML frameworks and programming, and hands-on work in deep learning, computer vision, NLP, or signal processing.

Lead the design, build, and operation of enterprise-scale Databricks Lakehouse platforms at Qualcomm, driving AI-native development, MLOps practices, and cloud modernization. This hands-on Staff IT Engineer role requires 4–5 years of Databricks experience and 8–12 years total IT background, with responsibility for platform architecture (Delta Lake, Unity Catalog, Workflows), machine learning enablement (MLflow, Mosaic AI), AIOps automation, and technical mentoring of engineers.…See more You will establish engineering standards, optimize platform performance and cost, implement observability and self-healing automation, and drive adoption of generative AI tools and intelligent automation across development teams.

As a Forward Deployed Engineer IV in Applied AI at Google Cloud, you will lead end-to-end engineering for production conversational AI systems, transforming prototypes into scalable agentic workflows deployed at customer sites. You'll architect multi-agent systems using frameworks like ReAct, optimize RAG implementations, debug agent logic across microservices, and connect AI systems to enterprise infrastructure.…See more The role requires 8+ years of software development (Python or similar), experience with infrastructure-as-code (Terraform), cloud platform deployment (GCP), and hands-on full-stack application development. You'll travel up to 50% and work directly with customers to establish production AI journeys while identifying patterns that feed back into product improvements.

The MLOps Engineer II designs, develops, and operates scalable machine learning infrastructure and deployment pipelines on AWS, working with data scientists and cloud engineers to productionize ML models. Responsibilities include building ML pipelines using SageMaker, Lambda, Step Functions, and S3; implementing CI/CD pipelines with GitHub and AWS tools; developing Infrastructure-as-Code using CloudFormation, Terraform, or AWS CDK; and ensuring production ML systems are reliable, secure, and cost-efficient.…See more The role requires strong Python coding skills, hands-on AWS experience, production ML deployment experience, and the ability to independently implement technical solutions. Candidates will also lead FinOps optimization, implement monitoring and alerting, ensure security and compliance, and mentor junior engineers.

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.