Job Description:
The AI Engineer (GenAI & Agentic Systems) designs, prototypes, and builds generative AI and agent‑based automations for knowledge work, decision support, and business workflows, and evaluates AI platforms and AI features embedded in 3rd party products. This role experiments with and develops POCs/MVPs with large language models (LLMs), small language models (SLMs), retrieval‑augmented generation (RAG), and agentic AI patterns.
**Key Responsibilities
GenAI POC and MVP Development**
- Design and implement GenAI‑powered automations using LLMs/SLMs, tools, and agents.
Inference Pipelines for GenAI
- Design and build GenAI inference pipelines that:
- Accept structured and unstructured inputs
- Apply prompt templates and system instructions
- Invoke LLMs and tools
- Post‑process outputs into reliable, auditable results.
- Support real‑time, batch, and asynchronous inference patterns.
Prompt Engineering & Feature Engineering (Data)
- Design and test prompt templates for data.
- Implement data feature engineering for GenAI.
Agentic Architecture & Tool Integration
- Design and prototype/develop agentic automations that decompose tasks, plan and reason over steps, invoke tools and APIs, and handle errors and retries.
- Integrate agents with AI platform and other source APIs.
- Apply guardrails to control agent autonomy, cost, and risk.
Evaluation of SaaS / COTS GenAI Capabilities
- Technically evaluate GenAI and agentic features embedded in third‑party SaaS/COTS products, including:
- LLM, SLM usage patterns and limitations
- Prompt and agent customization options
- API accessibility and automation readiness
- Observability and audit controls.
- Provide technical input to architecture, procurement, and governance decisions.
**Required Qualifications
Technical Skills**
- Strong proficiency in Python and API‑based service development.
- Hands‑on experience with LLMs, SLMs, RAG, prompt engineering, and Agentic AI frameworks or patterns.
- Experience building inference pipelines for GenAI.
- Strong understanding of API‑first and event‑driven architectures.
- Experience integrating AI services with enterprise systems and SaaS platforms.
Conceptual & System Skills
- Deep understanding of GenAI inference vs traditional deterministic systems, prompt‑centric and context‑centric design, and agent autonomy vs control tradeoffs.
- Ability to translate experimentation into reliable, automated AI systems.
- Strong collaboration skills across engineering, product, data, and governance teams.
Preferred Qualifications
- Experience building and maintaining GenAI systems in enterprise or regulated environments.
- Familiarity with:
- Vector databases and document retrieval systems
- Cost and latency optimization for LLMs and SLMs
- Monitoring and evaluation of GenAI outputs.
- Experience assessing vendor‑provided GenAI platforms and features.
Exposure to AI risk management or responsible AI practices.