14 hours ago
Dubai, United Arab EmiratesStaff+
Responsibilities
- Understand business needs with stakeholders and take AI solutions from discovery through production.
- Design, build, deploy, and continuously improve enterprise-grade agentic AI applications.
- Build agents with tool and API calling, context management, exception handling, human-in-the-loop workflows, guardrails, and structured outputs.
- Design RAG pipelines covering ingestion, chunking, embeddings, vector search, retrieval tuning, grounding, and source traceability.
- Build MCP-based integrations with REST/OpenAPI APIs, webhooks, event-driven systems, secure authentication, and reusable interfaces.
- Own testing, evaluation, observability, logging, versioning, feedback loops, reliability, accuracy, latency, security, and cost.
- Apply security, privacy, access control, auditability, responsible AI, and governance across deployments.
- Set technical direction and standards, make architecture and build-versus-buy decisions, and lead delivery with external AI platforms and vendors.
- Design multi-agent and agent-to-agent systems and their evaluation frameworks.
- Mentor engineers, review work, and raise squad standards for quality, security, and cost.
Requirements
- 8+ years building production-grade software, including 4+ years with GenAI, LLMs, and applied ML and at least 1 year of hands-on agentic AI experience.
- Track record of setting technical direction and shipping agentic systems at scale.
- Hands-on experience or strong working knowledge of MCP (Model Context Protocol).
- Strong Python skills and strong knowledge of at least one of TypeScript, JavaScript, Java, or C#.
- Experience with async programming, FastAPI, Pydantic, Git, testing, error handling, and logging.
- Experience with at least one agent framework or enterprise AI platform, such as LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock AgentCore, or Google Vertex/Gemini.
- Experience with a vector database or search platform, such as Azure AI Search, pgvector, Pinecone, Weaviate, or OpenSearch.
- Experience integrating enterprise systems and deploying cloud-based containerized applications with monitoring and observability.
- Knowledge of trade-offs involving latency, quality, cost, reliability, security, privacy, responsible AI, and governance.
- Fluent English and ability to work in a culturally diverse, international team.
- Preferred experience includes aviation or airline operations, classical machine learning, data science, full-stack development, voice AI, email automation, CRM integrations, workflow automation, multilingual agents, simulation-based testing, regression testing, agent evaluation, and vendor delivery leadership.
- Master's degree in Computer Science, Software Engineering, Data Science, AI/ML, or a related technical field, or equivalent practical experience; relevant cloud-AI, GenAI, agentic-AI, or MLOps certifications are advantageous.
Tech Stack
Categories
Forward Deployed
About Cognizant
Cognizant is a public IT services and consulting firm that designs, builds, and runs enterprise technology, including digital engineering, cloud modernization, data/AI, and managed services. It sells consulting, systems integration, and outsourcing on multi-year engagements to large enterprises in healthcare, banking, retail, communications, and manufacturing. Founded in 1994 and headquartered in Teaneck, New Jersey, Cognizant is NASDAQ-listed (CTSH) and a Fortune 500 company.