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Genai Engineer Jobs in California (NOW HIRING)

We're hiring a Staff Software Engineer to pioneer this frontier--not building foundation models from scratch, but expertly integrating GenAI and LLMs into production e-commerce systems that scale to ...

We're hiring a Staff Software Engineer to pioneer this frontier--not building foundation models from scratch, but expertly integrating GenAI and LLMs into production e-commerce systems that scale to ...

We're hiring a Staff Software Engineer to pioneer this frontier--not building foundation models from scratch, but expertly integrating GenAI and LLMs into production e-commerce systems that scale to ...

We're hiring a Staff Software Engineer to pioneer this frontier--not building foundation models from scratch, but expertly integrating GenAI and LLMs into production e-commerce systems that scale to ...

We're hiring a Staff Software Engineer to pioneer this frontier--not building foundation models from scratch, but expertly integrating GenAI and LLMs into production e-commerce systems that scale to ...

We're hiring a Staff Software Engineer to pioneer this frontier--not building foundation models from scratch, but expertly integrating GenAI and LLMs into production e-commerce systems that scale to ...

We're hiring a Staff Software Engineer to pioneer this frontier--not building foundation models from scratch, but expertly integrating GenAI and LLMs into production e-commerce systems that scale to ...

Showing results 21-40

Genai Engineer information

What are some typical challenges a GenAI engineer faces when deploying AI models in production environments?

GenAI Engineers often encounter challenges such as ensuring model scalability, addressing bias in generated outputs, and maintaining performance consistency in real-world applications. Deploying generative AI models requires careful monitoring to prevent unexpected or inappropriate outputs, as well as efficient resource management to handle large-scale computations. Collaborating closely with data engineers, product managers, and ML operations teams is essential to streamline deployment pipelines and quickly resolve issues that arise in live environments.

What is a GenAI engineer?

A GenAI Engineer is a professional who specializes in designing, developing, and deploying generative artificial intelligence (AI) models and applications. This role involves working with advanced machine learning techniques, such as large language models and generative adversarial networks, to create systems that can generate text, images, code, or other content. GenAI Engineers collaborate with data scientists, software engineers, and product teams to integrate AI capabilities into products and services, ensuring ethical use and scalability. They also stay updated on the latest developments in AI research to continually improve model performance and effectiveness.

What is the difference between Genai Engineer vs Data Scientist?

AspectGenai EngineerData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; experience with AI/ML frameworksDegree in Data Science, Statistics, or related fields; strong programming skills
Work EnvironmentDevelops AI models, fine-tunes generative AI systems, collaborates with AI teamsAnalyzes data, builds predictive models, interprets complex datasets
Employer & Industry UsageTech companies, AI startups, research labs focusing on generative AIFinance, healthcare, marketing, and tech firms analyzing data for insights

While both roles require strong technical skills and a background in data or AI, Genai Engineers focus on developing and deploying generative AI models, whereas Data Scientists analyze data to extract insights and build predictive models. The roles often overlap but serve different primary functions within AI and data-driven organizations.

What are the key skills and qualifications needed to thrive as a GenAI engineer, and why are they important?

To thrive as a GenAI Engineer, you need expertise in machine learning, deep learning, and programming languages such as Python, along with a solid understanding of generative models like GANs and transformers. Familiarity with frameworks such as TensorFlow or PyTorch, and experience with cloud platforms and MLOps tools, are highly valuable; advanced degrees or certifications in AI or data science are often preferred. Strong problem-solving, creativity, and communication skills help GenAI Engineers design innovative solutions and effectively collaborate with multidisciplinary teams. These skills ensure the development of robust, scalable generative AI systems that address complex real-world challenges.
What are popular job titles related to Genai Engineer jobs in California? For Genai Engineer jobs in California, the most frequently searched job titles are:
What cities in California are hiring for Genai Engineer jobs? Cities in California with the most Genai Engineer job openings:
Infographic showing various Genai Engineer job openings in California as of August 2026, with employment types broken down into 92% Full Time, 2% Part Time, and 6% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution.

$150K - $170K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 29 days ago


Job description

Must Have Technical/Functional Skill
Senior AI Engineer with deep, hands on expertise in Generative AI, multi agent orchestration, and LLM based systems to design, build, and scale secure, production grade AI platforms. The role requires strong engineering rigor, practical experience beyond PoCs, and the ability to operationalize agentic AI in complex enterprise environments.
Roles & Responsibilities
Core Skills & Responsibilities
Generative AI & LLM Engineering
• Strong hands on experience building LLM powered applications for reasoning, summarization, Q&A, and content generation
• Expertise in prompt engineering, prompt optimization, and systematic prompt evaluation
• Ability to generate structured and deterministic outputs for downstream consumption
• Apply grounding, response validation, and hallucination mitigation techniques
Multi Agent Orchestration & LangGraph (Must Have)
• Strong hands on experience designing and implementing multi agent AI systems
• Practical experience working with agent orchestration frameworks, specifically LangGraph
• Design and implement:
o Orchestrator / supervisor agent patterns
o Intent routing, task decomposition, and agent sequencing
o Dependency management and agent handoffs
• Implement tool calling patterns, shared context management, and output aggregation across agents
• Experience managing agent state, memory, and execution flow in production environments
Retrieval Augmented Generation (RAG)
• Build and optimize RAG pipelines across large unstructured datasets
• Hands on experience with:
o Embedding strategies
o Vector databases and semantic search
o Chunking, ranking, and metadata based retrieval
• Ensure responses are traceable, explainable, and source grounded
AI Application Engineering
• Strong backend engineering skills using Python
• Design modular, API first AI services using scalable architectures
• Integrate AI services with external systems and tools via secure APIs
• Efficient handling of long running agent workflows and asynchronous execution
Cloud Native AI & Platform Skills
• Experience deploying AI solutions on cloud native platforms
• Familiarity with:
o Containerized AI workloads
o CI/CD pipelines for AI and agent services
o Scalable, fault tolerant system design
• Experience managing structured and unstructured data storage for AI workloads
AI Governance, Security & Reliability
• Implement guardrails and safety controls, including:
o Prompt injection prevention
o Access control and role based execution
o Output validation and policy enforcement
• Support observability for:
o Agent behavior and execution flow
o Confidence scoring and failure detection
o Usage patterns and anomaly detection
• Design AI systems that are auditable, explainable, and enterprise ready
Collaboration & Technical Leadership
• Provide technical leadership across AI and GenAI initiatives
• Review AI designs, orchestration flows, and implementation quality
• Mentor junior AI engineers on agent design and best practices
• Collaborate with architects, data scientists, and platform engineers
Required Qualifications
• Strong experience as a Senior AI / GenAI Engineer
• Proven, hands on experience with multi agent orchestration using LangGraph
• Advanced knowledge of:
o LLMs and agentic AI systems
o RAG architectures
o Python based AI development
• Experience delivering production grade AI systems, not just experiments
Preferred Qualifications
• Experience designing large scale agent based platforms
• Exposure to LLMOps / MLOps practices
• Familiarity with model and agent evaluation techniques
• Experience working in regulated or security sensitive environments
Salary Range: $150,000-$170,000 a year
TCS Employee Benefits Summary:
Discretionary Annual Incentive.
Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
Family Support: Maternal & Parental Leaves.
Insurance Options: Auto & Home Insurance, Identity Theft Protection.
Convenience & Professional Growth: Commuter Benefits & Certification & amp; Training Reimbursement.
Time Off: Vacation, Time Off, Sick Leave & Holidays.
Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.
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