Full-Stack AI Engineer

Cognizant Ace Team is an elite engineering initiative that brings together a select group of high-potential AI professionals to work on real-world client problems. As an Ace Full Stack AI Engineer, you’ll join a compact, high-performing team focused on delivering impactful solutions across industries. From day one, you’ll contribute to live production systems—building advanced AI solutions such as retrieval-augmented generation (RAG) pipelines, intelligent agent workflows, and applications powered by large language models. You’ll also collaborate directly with clients, demonstrating working solutions and driving outcomes. The program is designed around structured rotations across different domains, technologies, and problem areas. This approach helps you quickly develop both specialized expertise and broad technical exposure, enabling you to build a strong portfolio of real-world solutions in a much shorter time than traditional career paths.

AI-Driven Engineering Develop user interfaces, backend APIs, and complete workflows powered by LLMs, AI assistants, and autonomous agents Create effective prompt strategies, structured response formats, and tool-integration patterns for real-world deployment Build AI-centric systems such as RAG pipelines, agent-based workflows, and integrated tool ecosystems Embed fine-tuned or hosted AI models into scalable enterprise applications Solution Design & Architecture Quickly understand new codebases, team setups, and business problems within short timelines Convert business needs into practical AI-powered solution architectures with well-defined APIs and data flows Evaluate and choose appropriate approaches (e.g., model selection, RAG vs. agent-based systems) based on performance and cost considerations Actively contribute to architectural decisions and clearly document design choices and assumptions Quality & Governance Assess AI outputs for accuracy, reliability, and ethical considerations, identifying issues such as hallucinations or bias early Build and maintain automated testing processes for AI-enabled functionalities Set up monitoring, logging, and safety mechanisms to ensure transparency and traceability Follow and advocate for secure development practices and responsible AI usage Delivery & Client Impact Deliver high-quality AI-driven features and production-ready systems efficiently Develop quick-turnaround prototypes, proof-of-concepts, and client demonstrations Prepare clear documentation to enable client teams to maintain and scale solutions independently Communication & Collaboration Explain technical ideas effectively to both technical and non-technical audiences Lead product demos, walkthroughs, and client presentations Work closely with cross-functional teams and support team members through knowledge sharing and mentorship

Students graduating in 2026 with B.E/B. Tech/M.E/M. Tech degrees. Relevant experience in AI-enabled full stack development will be preferred.

1. Core AI & LLM Concepts (Must-Have) Focus on understanding—not just definitions, but when to use what. How LLMs work (tokens, embeddings, transformers basics) Prompt engineering (zero-shot, few-shot, chain-of-thought) RAG (Retrieval-Augmented Generation) — architecture, pros/cons, when to use Agentic workflows (tools, memory, planning) Fine-tuning vs prompting vs embeddings Vector databases (FAISS, Pinecone alternatives) Hallucinations: why they happen & how to reduce them Evaluation of AI outputs (accuracy, latency, cost) 2. Full Stack Development You’re expected to actually build systems, not just talk theory. Frontend React / modern JS frameworks API integration State management basics Backend REST API design Node.js / Python (FastAPI, Flask) Authentication (JWT, OAuth basics) Database design (SQL + NoSQL) 3. System Design (Very Important) This is where many candidates struggle. Designing scalable AI systems Data flow in RAG pipelines Caching strategies (for LLM responses) Handling latency & cost optimization Microservices vs monolith Logging, monitoring, observability 4. Practical AI Engineering They’ll check if you can actually build. Using OpenAI / open-source models LangChain / LlamaIndex concepts Building: Chatbots Document Q&A systems AI copilots Tool calling / function calling Embedding pipelines

1 Round : online Assessment 2 Round : Technical Interview 3 Round : Hr Round