About the role

This is a coding role. You will ship production code every day across the full stack and the AI layer. You take an ambiguous problem, prototype a working answer fast, then harden it into a multi-tenant, observable, enterprise-grade service. This role does not drift into slides.

We need someone who is genuinely full stack and genuinely AI-first. Not a backend engineer who has called an LLM API once, and not a researcher who has never carried a pager.

Core tech stack

Languages: Python and TypeScript, fluent in both, not passable in one. Node where it fits.

Frontend: React (or equivalent contemporary framework), streaming and async UX for AI interfaces.

Backend and APIs: REST, ideally GraphQL or gRPC. Async and event-driven patterns, caching, queueing.

Data: Relational modelling, vector search and retrieval, ETL and pipeline basics.

Cloud: AWS at depth, including Bedrock, Lambda, ECS or EKS, and managed data services. Working exposure to a second cloud (Azure or GCP).

DevOps: Docker and container orchestration, CI/CD, infrastructure as code with Terraform, CDK, or similar.

AI engineering you must have

Deep production experience with frontier models (Claude, GPT, Gemini, or comparable), running live, not in notebooks. Specifically:

- Agentic patterns, orchestration, tool and function calling
- Retrieval and RAG pipelines, context and memory design
- Structured output,



streaming, and fallback behaviour when the model gets it wrong
- Deterministic guardrails around non-deterministic systems
- LLM observability and tracing with LangSmith, Langfuse, or equivalent
- Evaluation as engineering practice: eval sets, release gates, regression suites
- Cost and latency engineering: token budgeting, prompt caching, batching, model routing
- Fluency with AI coding tools in your own workflow, with opinions on where they help and where they hurt

Also required

Multi-tenant SaaS: Real tenant isolation, per-tenant config, usage metering, tenant-scoped auth, cross-tenant migrations.

Testing: Unit, integration, and contract tests as how you move fast, not a tax on speed.

Security and compliance: Prompt injection, data leakage, and PII handling by design. Familiarity with the OWASP LLM risk categories, SOC 2, audit trails, data residency, and least-privilege access.

Ownership: Monitoring, alerting, incident response, and postmortems that change something.

Nice to have

MCP and tool servers for agentic systems, developer tooling or SDLC

platform experience, fine-tuning or serving open-weight models, B2B SaaS into regulated buyers (financial services or government), team lead or mentoring experience, open source or public technical writing.

📌 Senior Artificial Intelligence Engineer (Telangana)
🏢 YottaFlex AI Technologies
📍 Telangana