Responsibilities
Product & Business Partnership
· Co-own problem discovery with Product/Business: translate objectives into well defined, testable problem statements, user journeys, and technical hypotheses.
· Challenge assumptions and requirements using user insights, data realities, feasibility constraints, and system impact; recommend alternatives when they better achieve outcomes.
· Define and track success metrics (e.g., adoption, time saved, conversion, accuracy/quality, latency, reliability, cost-to-serve, risk reduction) and drive iteration when targets aren’t met.
· Communicate clearly with executive, business, and engineering audiences - articulating trade-offs, decisions, and rationale.
Solution Leadership
· Lead end-to-end solution for GenAI products: APIs, services, data pipelines, orchestration, LLM integration, retrieval, tool-calling, and UI/UX touchpoints as needed.
· Make disciplined architectural trade-offs across performance, reliability, cost, extensibility, maintainability, and time-to-value.
· Design for enterprise realities: identity/access, data residency, compliance constraints, multi-environment deployments, and integration with core systems.
· Drive platform thinking prioritize reusable components (libraries, templates, shared services) over one-off builds.
Agentic & Full-Stack GenAI Applications
· Build and scale agentic GenAI applications that solve multi-step workflows on cloud platforms such as AWS and Azure.
· Define and implement robust LLM patterns (prompting, RAG, tool orchestration, validation, and evaluation).
· Implement LLMOps and lifecycle governance for models and prompts, including versioning, evaluation and monitoring.
· Ensure solutions meet enterprise standards for quality, reliability, latency, and cost.
· Embed Responsible AI, security, and compliance by design, partnering with Risk, Legal, and Security.
Engineering Excellence & DevOps
· Provide hands-on leadership across the SDLC: design reviews, coding standards, testing strategy, code reviews, security reviews, and operational readiness.
· Establish robust CI/CD and standardized environments for services and AI components utilizing cloud services.
· Ensure operational excellence through observability, incident management, reliability practices, and cost optimization.
Required Knowledge:
· Proven track record in designing and building GenAI solutions, with a strong expertise in prompt engineering, LlamaIndex, Lang chain, RAG, Agentic AI workflow,
· Knowledge graph, AI red teaming libraries, LLM monitoring and evaluation.
· Over 7+ years of experience in the software development lifecycle as a developer, with a focus on writing production code.
· Must have hands-on experience working with software development toolkits, and devOps automation like Kubernetes, Airflow, Jenkins, Jira, Confluence and Git.
· Excellent programming skills in Python and proficiency in PySpark.
· Experience in Data Bricks, AWS bedrock, Microsoft Copilot Studio and Azure ML.
· Strong leadership skills with ability to influence the thought process and drive alignment.
· Excellent communication skills and a demonstrated ability to collaborate effectively with cross-functional teams.
· Self-motivated and highly driven individual who can thrive in ambiguous requirements.
About Cognizant:
Cognizant (Nasdaq: CTSH) engineers modern businesses. We help our clients modernize technology, reimagine processes and transform experiences so they can stay ahead in our fast-changing world. Together, we're improving everyday life. See how at www.cognizant.com or @cognizant.
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私たちについて：
コグニザント（NASDAQ: CTSH）は、AI builderおよびテクノロジーサービスプロバイダとして、AI投資を企業価値へとつなげるフルスタックのAIソリューションを提供しています。業界、業務プロセス、エンジニアリングに関する深い専門性を強みに、各企業固有のコンテキストをテクノロジーシステムに組み込み、人の力を最大限に引き出すとともに、具体的な成果の創出と、急速に変化する世界におけるグローバル企業の競争力維持を支援します。詳しくは、当社ウェブサイト www.cognizant.com をご覧ください。
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