Overview
On Site
Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - 12 Month(s)
Skills
AI
ML
LLM
agent
MCP
RAG
RetrievalAugmented Generation
Python
kubernetes
docker
Job Details
Key Responsibilities
- Design, build and operate MCP servers and MCP agents that host, orchestrate and monitor AI/agent workloads.
- Develop agentic AI, 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 (RetrievalAugmented 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, APIs, and SDKs for model access and agent orchestration.
- Design agent behavior, workflows and safety guards for agentic AI systems.
- Create, test and iterate prompt templates, evaluation harnesses and grounding/chainofthought strategies.
- Integrate LLMs and model providers (selfhosted and cloud APIs) 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, reranking 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 infrastructureascode 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 opensource vector stores and embedding providers.
- Familiarity with CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, or ArgoCD).
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