About the Role

MIRAI AI is a predictive Cyber Threat Intelligence (CTI) platform that combines LLM-powered agents, retrieval augmented reasoning, and multi-tenant enterprise SaaS. We're hiring a Senior AI Engineer to design and ship the systems at the core of the product: multi-agent orchestration, advanced RAG pipelines grounded in threat intelligence, self-hosted and frontier-model inference, and the tooling that connects our agents to live security data. This is a hands-on, deeply technical role.

You'll own AI systems end-to-end - prompt and context design, retrieval, tool calling, inference, and the evaluation that keeps all of it honest - and get them running reliably in production. We're looking for someone genuinely deep in applied LLM/agent/RAG engineering, and comfortable across the surrounding stack - including model serving and the AI-specific security a threat-intelligence pipeline demands. You don't need to be a platform/infrastructure specialist or an application-security engineer; we build those functions around you.

What You'll Build

- Agentic systems - multi-agent workflows (planning, reasoning, human-in-the-loop, memory, retry and failure recovery) that enrich IOCs, map activity to frameworks like MITRE ATT&CK;, and reason over threat actors and campaigns.
- Advanced RAG - hybrid retrieval (vector + keyword) over a graded, per-tenant intelligence corpus, with reranking, strong citation, and evidence-grounding so every claim is traceable to a source.
- Production LLM services - backends serving structured, tool-calling LLM workflows with Pydantic schema validation and graceful degradation.
- Inference & model serving - a two-tier model strategy: frontier APIs for reasoning and report generation, and self-hosted 7B–14B models (Ollama, vLLM, or equivalent) for high-volume classification, extraction, and summarization - with quantization, batching, and caching for cost and latency at volume.
- Agent tooling - tools, function-calling interfaces, and MCP server integrations that let agents access CTI data sources with tightly scoped permissions.

What You'll Do

- Shape, build, and operate agentic and RAG pipelines as production services, not prototypes.
- Make evaluation a first-class, CI-gated practice: golden datasets, RAG metrics (e.g., RAGAS), regression tests,



and red-teaming of agent and retrieval behaviour - wired in so regressions fail the build.
- Drive down hallucination and improve grounding, citation validity, and retrieval accuracy against measured outcomes.
- Ship LLM services with structured outputs and reliable tool calling, treating prompt and context engineering as versioned, tested artifacts - not ad-hoc strings.
- Harden the pipeline against prompt injection, and treat all ingested intelligence as untrusted input at every layer.
- Instrument everything - tracing, structured logging, per-tenant token metering, and cost/latency dashboards - so quality regressions and drift are caught before customers see them.
- Collaborate with CTI, product, and platform teammates to turn intelligence requirements into reliable AI capabilities.

Must-Have Qualifications (Core) These are the non-negotiables - we expect real production depth here.

- Expert Python - strong async experience, building services with FastAPI and Pydantic.
- Agentic systems in production - multi-agent orchestration, tool calling, agent memory, state/checkpointing, and failure recovery - with a modern framework such as LangGraph (or a defensible equivalent: LlamaIndex agents, Autogen, custom orchestration).
- Advanced RAG architectures - hybrid search, chunking strategies, reranking, retrieval evaluation, citation, and hallucination reduction.
- Vector databases - practical experience with pgvector (today's default), and/or Qdrant, Milvus, or Weaviate as scale requires.
- Knowledge graphs & graph-based reasoning - modelling entities and relationships (e.g., Neo4j) for retrieval and threat-actor correlation.
- Prompt & context engineering with structured outputs - reliable, schema-constrained LLM behaviour through tool calling; prompts and context handled as versioned, tested artifacts.
- LLM inference & serving - self-hosted deployment (Ollama, vLLM, or equivalent) on GPU, plus quantization,



batching, caching, and latency/cost optimisation at volume.
- AI evaluation discipline - golden datasets, precision/recall/F1, RAG evaluation (e.g., RAGAS), and regression testing wired into CI.
- AI security - defending against prompt injection and data exfiltration via tool use, scoping tool permissions, handling untrusted ingested content, and awareness of model supply-chain risk.
- Database fundamentals - PostgreSQL (with pgvector) and Redis.
- Production delivery - shipping containerised services with Docker and CI/CD.

Experience

- 5+ years of software engineering.
- 3+ years shipping production LLM / generative-AI applications, including hands-on RAG and agent work.
- Experience contributing to a scalable, multi-tenant SaaS product.

Robust Plus You won't have all of these - depth in a few is what we're after.

- MCP (Model Context Protocol) - building MCP servers and custom tools.
- Hugging Face & PyTorch - inference optimisation, fine-tuning (LoRA/QLoRA), embeddings, and reranking models.
- Search infrastructure - OpenSearch / Elasticsearch.
- MLOps - MLflow, experiment/model versioning, evaluation automation, drift monitoring.
- Observability stack - OpenTelemetry, Prometheus, Grafana, distributed tracing, AI-quality dashboards.
- Data engineering - streaming (Kafka / Redpanda), ETL/ELT, data validation, API ingestion, web scraping, document processing.
- CTI data substrate - working with OpenCTI, MISP, MITRE ATT&CK; STIX, CVE/NVD, EPSS, CISA KEV, and SSVC.

Nice to Have (Optional) Genuinely optional. Strength here is a bonus, not an expectation - we don't expect one person to own the platform or security functions.

- Platform / infrastructure - Kubernetes, Helm, Terraform, AWS or Azure, autoscaling, distributed and GPUcluster inference.
- Application & platform security - OAuth2/OIDC, JWT, RBAC/ABAC, secret management, multi-tenant isolation, audit logging, secure API design.
- CTI domain knowledge - threat actors and campaigns, IOC enrichment, TTPs, detection-engineering basics, SIEM/XDR integrations, dark-web and EASM exposure. (You'll pick a lot of this up on the job.)
- Frontend - React, Next.js, TypeScript, Tailwind, data visualisation for internal tooling and dashboards.

📌 Senior AI Engineer (Bengaluru)
🏢 Mirai Cyber
📍 Bengaluru