Senior Full‑Stack Engineer - Planning Solutions

Bahwan CyberTek; Chennai District; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Bahwan CyberTek in Chennai, India seeks a Senior Full-stack Engineer to build production-grade AI-enabled systems across frontend and backend stacks.

You will craft multi-agent workflows, manage memory and long-running tasks, and deploy via containerized cloud pipelines, ensuring reliability and performance.

Qualifications

• Bachelor's degree in CS, Engineering, Mathematics, or related field.
• Experience building production-grade SPA and backend services.
• Proficiency with Python and React/Angular.
• Experience deploying LLM-based applications in production.
• Knowledge of vector databases and modern data architectures.

Responsibilities

• Build High-Quality SPA across experiments to flagship products.
• Develop backend services and APIs powering AI applications.
• Design multi-agent workflows and orchestrate components.
• Implement stateful memory, long-running execution, and reliable fault handling.
• Ensure scalable, secure, and observable cloud deployments.

Skills

Python

React/Angular

TypeScript

FastAPI

LangGraph

AWS Bedrock

Langfuse

LLM in production

Education

Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field

Tools

Docker

Kubernetes

ECS

CI/CD

Job description

As a Senior Full-stack Engineer, you will build production-grade systems that go well beyond conversational assistants. This role sits at the intersection of software engineering, data engineering, and Agentic engineering, building stateful, goal-driven AI systems that can reason, plan, coordinate, and execute complex tasks with appropriate controls. You will collaborate closely with colleagues across Product, Design, Infrastructure, and other Application engineers. You will help enforce sound engineering practices, and ensure our AI-driven applications remain robust, maintainable, and performant. You must demonstrate a strong understanding of cloud architecture principles.

Primary Responsibilities

• Build High-Quality SPA: Design, develop, test, and maintain Single Page Applications ranging from experiments to flagship products.
• Build High-Quality Backend Systems: Design, develop, test, and maintain backend services and APIs that power AI applications, ensuring they are reliable, secure, and performant.
• Design and build multi-agent workflows: Build stateful agentic applications using agent orchestration frameworks capable of coordinating multiple autonomous components.
• State, memory, and long-running execution: Engineer control flows for non-deterministic systems, including message passing, persistent memory, recoverability, and interruptible execution for long-running tasks.
• Standardized tool interfaces: Establish universal interfaces between agents, enterprise data sources, and operational tools to ensure modularity, reusability, and consistent governance.
• Model integration and runtime optimization: Build routing and fallback strategies across multiple model endpoints; optimize context management, latency, and inference cost while maintaining reliability.
• Production deployment: Package and deploy workloads via containerization and cluster orchestration, using cloud-native services for scaling, isolation, and secure runtime operations.

Required Skills

• software engineering, data engineering, and/or machine learning engineering, with demonstrated ownership of production systems.
• Using Python, React/Angular, TypeScript, FastAPI, LangGraph, AI SDK, AWS, Langfuse
• Full-stack development: Advanced proficiency in Python and React/Angular for building modular, testable, maintainable production services.
• Generative AI in production: Proven experience building and deploying LLM-based applications and/or agentic systems in real-world environments.
• Storage and retrieval expertise: Proven experience designing AI-ready storage layers across vector databases, relational and NoSQL databases, and modern lakehouse/warehouse architectures.
• Cloud and infrastructure depth: Strong capability deploying and scaling services on major cloud platforms using containerization, cluster orchestration, CI/CD, and secure runtime practices.
• LLM systems understanding: Strong grasp of retrieval-augmented generation, embeddings, context strategies, prompt/system design, and failure modes in deployed systems.
• Hybrid engineering skillset: Ability to blend ML intuition (model behavior, uncertainty, evaluation) with software excellence (APIs, async systems, reliability engineering).
• Education: Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field (or equivalent experience).
• Technology Stack: Backend: Python, FastAPI Frontend: React or Angular, TypeScript AI/LLM: AWS Bedrock, LangGraph, RAG pipelines, embeddings Observability: Langfuse or similar LLM observability tools Infra: Docker, Kubernetes/ECS, CI/CD Data: Vector DBs (Pinecone, OpenSearch, Weaviate, etc.), SQL/NoSQL