AIML Engineer

We are seeking an experienced AIML Engineer to design build and operate Al/ML infrastructure and agentic systems. This role involves developing MCP servers and agents integrating LLMs and implementing RAG pipelines for production environments.
Key Responsibilities
* Design build and operate MCP servers and
MCP agents that host orchestrate and monitor Al/agent workloads.
* Develop agentic Al prompt engineering patterns LLM integrations and developer tooling for production use.
Own deployment scaling reliability and cost-efficiency on Kubernetes/Docker and Google Cloud with automated CI/CD
Design and implement RAG
(Retrieval-Augmented Generation) pipelines and integrations with vector stores and retrieval tooling; use LangChain and Langfuse for orchestration chaining and observability.
Core Responsibilities
Implement and maintain MCP server and agent code APls and SKs for model access and agent orchestration.
Design agent behavior workflows and
safety guards for agentic Al systems.
Create test and iterate prompt templates
evaluation harnesses and
grounding/chain-of-thought strategies.
Integrate LLMs and model providers
(self-hosted and cloud APls) with unified adapters and telemetry.
* Build developer tooling: CLI local runner
simulators and debugging tools for agents and prompts.
* Containerize services (Docker) manage
orchestration (Kubernetes/GKE) and optimize nodes autoscaling and resource requests.
* Ensure observability: logging metrics traces dashboards alerting and SLOs for model infra and agents.
Create runbooks playbooks and incident response procedures; reduce MTTR and perform postmortems.
* Design and maintain RAG workflows:
document chunking embeddings vector indexing retrieval strategies re-ranking and context injection.
* Integrate and instrument LangChain for
composable chains agents and tooling; use Langfuse (or equivalent tracing) to capture prompts model calls RAG traces and evaluation telemetry.
Required Skills & Experience
* 5 years of Strong Software Engineering (Python/NodeJS) system design and production service experience.
* 2 years of Experience with LLMs prompt
engineering and agent frameworks.
2 years of Experience Practical experience implementing RAG: embeddings vector DBs and retrieval tuning.
2 years of Experience with LangChain patterns and with toolchain telemetry (Langfuse or similar) for prompt/model traceability.
* 5 years of Experience with Kubernetes
Docker CI/CD and infrastructure-as-code experience.
2 years of Experience with Practical experience with Google Cloud Platform services
* 2 years of Experience with Observability testing and security best practices for distributed systems.
2 years of Experience with evaluating and mitigating retrieval/augmentation failures hallucinations and leakage risks in RAG systems.
* Familiarity with vendor and open-source
vector stores and embedding providers.
* Familiarity with CI/CD pipelines (Jenkins
GitHub Actions GitLab Cl or ArgoCD).