- Company Name
- Cisco
- Job Title
- Machine Learning Engineer
- Job Description
-
Job title: Machine Learning Engineer
Role Summary: Design, build, and operate production‑ready GenAI services, including retrieval‑augmented generation (RAG) pipelines, agentic frameworks, and developer SDKs. Work on large‑scale, multi‑tenant LLM inference, secure and observable AI workloads, and developer tools that enable rapid deployment of chat assistants and automation workflows.
Expectations: Deliver end‑to‑end features from design through production, iterate on performance and user experience, and advocate responsible AI practices. Collaborate closely with product, UX, and platform teams to ship features that drive business metrics.
Key Responsibilities:
- Implement and maintain GenAI APIs, chat assistants, and automation workflows across products.
- Build and refine RAG pipelines: retrieval orchestration, hybrid search, chunking & embeddings, and grounding with operational data.
- Contribute to agentic/multi‑agent workflows using LangChain, LangGraph, or similar frameworks, integrating with internal APIs and external systems.
- Develop developer‑facing SDKs, templates, and reference applications (Python/TypeScript) to simplify composition of tools, chains, and agents.
- Integrate evaluation stacks (e.g., LangSmith) to instrument prompts, capture traces, and run quality tests.
- Collaborate with product managers and UX to translate user stories into GenAI experiences, iterate based on feedback, and ship customer‑impacting features.
- Apply responsible AI principles: grounding, guardrails, access controls, and human‑in‑the‑loop flows.
Required Skills:
- 5+ years of backend or distributed systems engineering (or 2+ years with a master’s).
- Proficiency in Python or TypeScript/JavaScript (or Go/Java) with strong software design and debugging skills.
- Hands‑on experience with LLM APIs (OpenAI, Claude, Bedrock, Llama, etc.) and production features.
- Knowledge of REST/gRPC microservices, testing, deployment, and basic observability (logs/metrics).
- Experience with RAG systems and vector databases (Weaviate, Qdrant, Milvus, FAISS).
- Exposure to agentic frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel) and evaluation platforms (LangSmith).
- Familiarity with developer tooling: SDKs, templates, shared libraries.
- Understanding of AI safety, governance, RBAC, and data privacy.
Required Education & Certifications:
- Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
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