Agentic AI / Prompt Engineer
at 7hadex
India, remote, full time
seen 46d ago
- Vector databases
- Prompt Engineering
- DeepSeek
- LangChain
- RAG
- OpenAI
- Anthropic
- MCP
- Fine-tuning
- LlamaIndex
- Python
About the role
Agentic AI / Prompt Engineer
Data & AIIndia (Remote)Full-time
Make large language models reliable. You will design agentic workflows and prompt systems that produce safe, accurate, and cost-effective results, and you will prove it with evaluation, not vibes.
This is a frontier role. You will build the agents and prompt systems behind our AI products and client work, deciding when to use tools, memory, planning, and retrieval, and putting guardrails and evaluation around all of it. You should be equally comfortable crafting a prompt and reasoning about failure modes, cost, and latency.
What you would do
* Design multi-step agent workflows with tool use, memory, planning, and retrieval. * Engineer, version, and systematically evaluate prompts against measurable criteria. * Build with frameworks such as LangChain, LlamaIndex, or native agent SDKs. * Stand up RAG pipelines and the evaluation harnesses that keep them honest. * Reduce hallucination and add guardrails for safety, privacy, and brand. * Tune for latency and token cost without sacrificing quality. * Instrument and observe agents in production and iterate on real traces. * Document patterns so the rest of the team can build reliable LLM features.
What they are looking for
* Demonstrated experience building and shipping real LLM applications. * Strong prompt engineering instincts paired with a rigorous evaluation mindset. * Python and comfort integrating model APIs (OpenAI, Anthropic, DeepSeek, and similar). * A working understanding of RAG, tool use, function calling, and agent patterns. * The judgment to know when an agent is the right tool and when it is not.
* Vector databases, MCP, structured output, and light fine-tuning. * Eval frameworks, tracing, and observability for LLM apps. * A point of view on agent reliability and where it breaks.
What success looks like
* In 30 days: you have shipped an evaluated prompt or agent into a product. * In 90 days: you own an agentic feature with a real eval suite behind it. * In 6 months: our LLM features are measurably more reliable because of your systems.
Read from 7hadex.com, last seen 4d ago.