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Sdlc Jobs in California (NOW HIRING)

Santa Clara, CA OR Minneapolis, MN Job Summary As a Principal AI Driven SDLC Coach, you serve as the senior technical authority responsible for end-to-end coaching and governance of AI-driven full ...

ABOUT THE INFRA & SDLC TEAM Trustly's SDLC & Infrastructure team shapes how every engineer at Trustly builds and ships software. The team owns the software development lifecycle globally -- developer ...

.NET Developer

Mountain View, CA · On-site

$57 - $75.25/hr

NET 4.0 and 4.5 frameworks Strong understanding of software development life cycle, SDLC analysis/requirements, design, development, testing, and implementation Familiarity with government SELC ...

BI Project Manager

San Ramon, CA · On-site

$110K - $130K/yr

... SDLC) Strong experience delivering projects using a waterfall methodology Proven track record of proactively managing risks Extremely strong verbal, written and presentation skills (clear, concise ...

Incident, Problem, Change, Configuration, Asset management based on the ITIL framework; and Software Development Life Cycle (SDLC) and Business Rules Management System (BRMS). JOB DUTIES ...

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Sdlc information

See California salary details

$49

$62

$69

How much do sdlc jobs pay per hour?

As of Aug 27, 2026, the average hourly pay for sdlc in California is $62.67, according to ZipRecruiter salary data. Most workers in this role earn between $51.35 and $69.81 per hour, depending on experience, location, and employer.

What is SDLC?

SDLC stands for Software Development Life Cycle. It is a systematic process used by software developers and engineers to design, develop, test, deploy, and maintain software applications. The SDLC consists of several distinct phases, including requirement gathering, planning, designing, coding, testing, deployment, and maintenance. Following the SDLC helps ensure that software is delivered efficiently, meets customer requirements, and is of high quality.

What are some common challenges faced by professionals working within the Software Development Life Cycle (SDLC) process?

Professionals involved in the SDLC often encounter challenges such as coordinating communication among cross-functional teams, managing shifting project requirements, and ensuring adherence to timelines without compromising quality. Balancing thorough documentation with agile practices can also be difficult, as can maintaining consistent testing and deployment practices across projects. Being proactive in addressing these challenges through regular meetings, clear documentation, and collaborative tools can help teams stay aligned and deliver successful software solutions.

What are the key skills and qualifications needed to thrive as an SDLC manager, and why are they important?

To thrive as an SDLC Manager, you need a strong background in software development methodologies, project management, and process optimization, often supported by a degree in computer science or a related field. Familiarity with project management tools (like Jira or Trello), version control systems (such as Git), and certifications like PMP or Agile/Scrum Master are highly valued. Leadership, communication, and problem-solving abilities are essential soft skills to effectively coordinate teams and manage project timelines. These skills ensure efficient software delivery, high-quality products, and alignment between technical teams and business objectives.

What is the difference between Sdlc vs Software Tester?

AspectSdlcSoftware Tester
Primary RoleDefines and manages the software development process from planning to deploymentTests software to identify bugs and ensure quality
Required SkillsProject management, requirements analysis, development lifecycle knowledgeTesting methodologies, defect tracking, attention to detail
Work EnvironmentCollaborates with developers, project managers, and stakeholdersWorks closely with developers and QA teams during testing phases
CertificationsPM certifications, SDLC frameworks knowledgeISTQB, CSTE certifications often preferred

While SDLC (Software Development Life Cycle) focuses on managing the entire software development process, a Software Tester specializes in evaluating the software to ensure quality. Both roles are essential in software projects, with SDLC providing the framework and Software Testers executing testing activities within that framework.

Is there a certification for SDLC?

There is no specific certification for the Software Development Life Cycle (SDLC) itself, but professionals often pursue certifications in related areas such as project management (PMP), Agile (CSM), or specific development methodologies to demonstrate their expertise in SDLC processes. These certifications can enhance understanding of best practices and improve job prospects in roles involving SDLC management.
Infographic showing various Sdlc job openings in California as of August 2026, with employment types broken down into 84% Full Time, 6% Part Time, and 10% Contract. Highlights an 76% Physical, 9% Hybrid, and 15% Remote job distribution, with an average salary of $130,358 per year, or $62.7 per hour.

Agile Coach

Santa Clara, CA • On-site

Full-time

Posted 8 days ago


Job description

*Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.*


Location: Santa Clara, CA OR Minneapolis, MN


Job Summary

As a Principal AI Driven SDLC Coach, you serve as the senior technical authority responsible for end-to-end coaching and governance of AI-driven full Software Development Lifecycle (SDLC). You design robust engineering guardrail harnesses and deliver structured hands-on coaching covering every SDLC phase. You standardize repeatable, secure, production-grade AI-augmented workflows for engineering teams, mitigate LLM hallucinations, technical debt, security vulnerabilities and inconsistent deliverables across the entire development lifecycle, while empowering engineers to maximize AI efficiency without compromising software quality, compliance and stability.


Key Responsibilities

Full AI-Driven SDLC Coaching & Hands-On Enablement

Lead formal training, 1:1 deep coaching, team workshops and live code clinics covering the complete AI-powered SDLC workflow:

  • Spec & Requirements Collection: Coach structured prompt design, user story refinement, ambiguous requirement decomposition, and AI-assisted formal specification drafting; guide teams to avoid vague inputs that cause flawed downstream deliverables.
  • Security Analysis & Threat Modeling: Train engineers to leverage AI tools for automated vulnerability scanning, attack surface mapping, OWASP compliance checks, data leakage risk assessment at the design phase (shift-left security via AI).
  • Implementation Planning: Guide AI-assisted architecture drafting, task breakdown, milestone scheduling, dependency mapping and modular development planning to prevent bloated or unmaintainable AI-generated solutions.
  • AI Code Generation: Establish disciplined vibe coding practices: structured prompt chaining, context injection, incremental code generation, and constrained model output to reduce redundant, buggy or non-idiomatic code.
  • Code Review Governance: Coach human-in-the-loop AI code auditing; build checklist-driven review frameworks to validate logic correctness, readability, performance and compliance of LLM-generated code.
  • Unit & Integration Testing: Train teams to use AI for test case auto-generation, edge case enumeration, mock data creation, automated test coverage validation and regression test suite construction.
  • Automated Documentation Generation: Standardize AI workflows for API docs, design docs, runbooks, comment blocks and release notes; ensure auto-generated documentation stays consistent with actual implemented code.
  • CI/CD Pipeline AI Integration: Coach embedding AI tools into build pipelines: pre-commit validation gates, in-flight code scanning, test auto-execution, artifact auditing and deployment approval automation within CI/CD workflows.


AI SDLC Harness Architecture & Tooling Build

  • Design, develop and maintain enterprise-grade technical harnesses that enforce guardrails across every SDLC stage listed above; embed automated validation gates to block unvetted AI outputs early in the lifecycle.
  • Integrate code LLMs, static/dynamic analysis tools, security scanners, test runners and doc generators into unified pipeline tooling natively hooked into existing CI/CD platforms.
  • Build observability dashboards to measure SDLC efficiency metrics: requirement clarity pass rate, security flaw escape rate, code rewrite overhead, test coverage ratio, documentation completeness and pipeline failure frequency caused by unregulated AI coding.
  • Continuously refine harness rules to counter LLM hallucinations, incomplete logic and insecure auto-generated artifacts across all development phases.


Enterprise Standardization & Compliance Governance

  • Author playbooks, prompt libraries, checklists and workflow templates for each AI SDLC stage for backend, frontend, cloud-native and embedded engineering teams.
  • Collaborate with cybersecurity, legal, DevSecOps and compliance teams to bake license auditing, IP validation, sensitive data filtering and regulatory requirements into AI SDLC harness gates.
  • Enforce mandatory human review gates for high-risk modules (authentication, payment processing, PII handling) at every SDLC checkpoint regardless of AI automation maturity.


Required Qualifications

  • Software engineering experience with complete hands-on SDLC delivery
  • Proven expertise across requirements gathering, security design, implementation planning, formal code review, manual/automated testing, technical writing and end-to-end CI/CD pipeline design for production systems.
  • Deep practical AI coding & LLM workflow expertise
  • Hands-on experience leveraging code-generating LLMs across all SDLC phases; advanced prompt engineering, output constraint design, and mitigation of AI hallucinations and logical defects.
  • Experience building custom wrapper tooling/harnesses to govern and validate AI outputs in pipelines.


Core Competencies

  • Lifecycle-first mindset: prioritizing full SDLC robustness over isolated fast code generation
  • Structured coaching style adaptable for junior to staff-level engineers
  • Strategic risk balancing: accelerating delivery via AI while locking in security, maintainability and compliance
  • Strong cross-team communication and technical documentation capabilities



Kaleidoscope, an Infosys Company, is an equal opportunity employer, and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, spouse of protected veteran, or disability.