What are the responsibilities and job description for the Agentic AI Engineer (Only W2 contract) position at Voto Consulting LLC?
Summary
- Engineering role (8 years) to design, build, and operate production grade agentic AI systems and multi agent architectures that leverage GenAI and large language models (LLMs) to automate complex workflows and deliver measurable business outcomes.
Key Responsibilitie
- Design and implement multi agent orchestration, task decomposition, and agent coordination patterns for real world applications.
- Integrate LLMs and multi model pipelines (generation, retrieval, vision, tool use) into agent workflows.
- Build RAG, memory, and long context solutions using vector databases and embeddings to provide agents with persistent knowledge.
- Productionize and scale inference pipelines: containerization, autoscaling, cost optimization, and latency SLAs.
- Instrument observability and evaluation: automated evals, A/B testing, logging, tracing, and safety/guardrails.
- Collaborate cross functionally with product, ML research, and SRE to define requirements, KPIs, and deployment plans.
Required Qualifications
- 08 years software engineering experience with a minimum of several years focused on agentic AI, multi agent systems, or GenAI productization.
- Proven track record deploying LLM based systems to production with measurable impact.
Required technical skills
- Agentic AI / Multi Agent Systems, GenAI, LLMs (model integration, prompt engineering, chain-of-thought design.
- Retrieval Augmented Generation (RAG), vector databases (Pinecone, Weaviate, Chroma, Qdrant) and embeddings.
- Agent orchestration frameworks (e.g., LangChain, LangGraph, AutoGen, or equivalent.
- Programming: Python (primary); TypeScript or Go a plus.
- Deployment: Docker, Kubernetes, CI/CD, cloud platforms (AWS/GCP/Azur).
- Observability & evaluation: automated eval frameworks, logging, metrics, and safety tooling.
Preferred qualifications
- Experience with multi model routing, tool enabled agents, RLHF or reward modeling, and cost aware inference strategies.
- Background in designing agent safety, access control, and compliance for production systems.
- Strong communication skills and experience mentoring engineers.
Success metrics
- Reliable, scalable multi agent pipelines in production with clear KPIs (latency, cost per request, task success rate, user satisfaction.
- Demonstrable improvements in automation, throughput, or resolution time attributable to agentic solutions.