Enterprise AI Architect JD:
What You Will Do
• Design and implement context architectures for LLM apps and agents: schemas, memory patterns, context assembly, and context window management.
• Build and optimize RAG pipelines (chunking, embeddings, hybrid retrieval, re-ranking) and validate quality using repeatable evaluation harnesses.
• Develop system prompts, prompt libraries, and structured output patterns; harden solutions against prompt injection and jailbreak attempts.
• Implement agentic workflows and tool-use integrations (APIs, function calling, workflow engines) with clear guardrails and observability.
• Engineer memory and persistence patterns (session memory, episodic recall, vector memory) appropriate for enterprise privacy and retention needs.
• Work with enterprise data and app teams to connect AI solutions to real systems (ERP/SCM/CRM, data lakes/warehouses), ensuring secure access and correct semantics.
• Collaborate with delivery leads to break work into stories, estimate effort, and drive day-to-day execution; mentor engineers through reviews and pairing.
• Use systematic optimization (prompt/context tuning, retrieval experiments, DSPy-style approaches) to improve reliability, latency, and cost.
What You Bring
AI Engineering Skills
• Strong Python skills and ability to ship production services.
• Hands-on expertise with RAG: embeddings, vector stores, retrieval strategies, re-ranking, and grounding techniques.
• Strong prompt and context engineering: system prompts, structured outputs, tool-use prompting, and context assembly patterns.
• Experience building agentic systems with orchestration frameworks (or custom implementations) and designing safe tool integrations.
• Awareness of security threats (prompt injection, data exfiltration) and ability to implement practical mitigations and guardrails.
Enterprise Integration Background
• Experience integrating AI apps with enterprise services, data sources, and identity (SSO/IAM), including secure network and secrets handling.
• Ability to work with structured and unstructured enterprise data; understand governance/lineage enough to avoid incorrect or unsafe data use.
• Comfort operating within enterprise SDLC controls: CI/CD, change management, security reviews, and production incident response.
• Working knowledge of enterprise workflows and process context so AI solutions map to real operations and decision points.
Tools & Platforms
• Python; common LLM/RAG frameworks (LangChain, LlamaIndex, Haystack or equivalent).
• Vector databases and search stacks (pgvector, Pinecone, Weaviate, Milvus, Elasticsearch/OpenSearch).
• Memory and state management approaches (session stores, vector memory, durable stores) appropriate for privacy and retention constraints.
• Evaluation and observability tooling (RAGAS, LangSmith/Phoenix or equivalent) and ability to build custom eval pipelines.
Preferred Qualifications
• B.Tech / M.Tech in CS, Engineering, or Linguistics; research background in NLP or information retrieval is a plus.
• Published work, open-source contributions, or internal frameworks related to context management or prompt engineering.
• Prior consulting or professional services experience — ability to adapt context design to diverse client environments quickly
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。
雇用に関する追加情報
本募集に記載されている報酬情報は、掲載日時点で正確なものです。Cognizantは、適用される法令に従い、いつでも本情報を変更する権利を留保します。
応募者は、対面またはビデオ会議による面接への参加を求められる場合があります。また、各面接の際に、現在有効な州政府または政府発行の身分証明書の提示を求められる場合があります。
Cognizantは機会均等雇用主です。応募および選考において、人種、肌の色、性別、宗教、信条、性的指向、性自認、国籍、障がい、遺伝情報、妊娠、退役軍人の地位、その他連邦法・州法・地方自治体の法律により保護されるいかなる特性に基づく差別も行いません。
