Were looking for an AI Application Developer to design, build, and operate production-grade AI applications that integrate contemporary agent/workflow capabilities. Youll develop performant backends in Python and services in Java, craft intuitive React frontends, and leverage LangChain with the Model Context Protocol (MCP) to connect LLMs with secure, real-world tools and data.

What You’ll Do

- Design & build AI apps endtoend: Python/FastAPI services, React UI, and Java microservices where needed; ship reliable features with strong observability and SLIs/SLOs.
- Implement MCP integration: Build/consume MCP clients & servers, expose tools/resources securely, and orchestrate multistep workflows for agents and deterministic pipelines.
- Deliver RAG & retrieval pipelines: Use embeddings, chunking, and vector stores; optimize prompt, context windows, and latency for real users.
- Productionize responsibly: Own CI/CD, canary releases, monitoring/alerting, incident response, and postmortems; meet security & compliance standards.
- Collaborate crossfunctionally: Partner with PMs, data/ML engineers, SRE/DevOps, risk/controls, and business stakeholders to land value safely in production.
- Continuously improve: Benchmark models, run A/Bs, refine prompts/tools,



and reduce total cost of ownership (TCO) through caching, batching, and efficient infra.

Required Qualifications

- 6–7 years professional experience building production applications with React (TypeScript preferred) and Python (FastAPI/Flask/Django).
- 2+ years hands-on experience building LangChain applications (chains, tools, agents, callbacks, tracing) and deploying them to production.
- RAG at scale using vector stores (e.g., Redis with RediSearch/RedisJSON, Pinecone) and embedding strategies; experience tuning latency/cost/correctness.
- MCP (Model Context Protocol) working knowledge: client/server patterns, tool/resource catalogs, JSONRPC 2.0, transports (STDIO/HTTPSSE), and secure capability exposure.
- Strong fundamentals in API design, microservices, event management, and data modeling.
- Proven track record operating apps in production (SLA/SLO ownership, logs/metrics/traces, oncall/incident management, resilience testing).
- Java experience (e.g., Spring Boot) sufficient to collaborate with/extend JVM services where required.
- Proficiency with Git, CI/CD, testing (unit/contract/integration), and Agile delivery.

📌 Artificial Intelligence Developer (Bengaluru)
🏢 Cognizant
📍 Bengaluru