- Company Name
- Thunderhawk Technology Partners
- Job Title
- Full-Stack GenAI Engineer
- Job Description
-
**Job Title:** Full‑Stack GenAI Engineer
**Role Summary:**
Design, develop, and deploy end‑to‑end generative AI solutions, including LLM‑based applications, retrieval‑augmented generation (RAG) systems, and scalable cloud infrastructure. Blend software engineering, machine learning, and prompt engineering to deliver production‑grade AI products and mentor junior team members.
**Expectations:**
- Deliver performant, secure AI services from concept to production.
- Optimize model inference (e.g., quantization, latency).
- Ensure reliability through monitoring, testing, and telemetry.
- Collaborate with cross‑functional teams and contribute to open‑source/internal research.
**Key Responsibilities:**
- Architect and implement agentic LLM systems using LangChain, FastAPI, and front‑end stacks (NextJS/React).
- Build and maintain RAG pipelines with hybrid sparse‑dense retrieval, semantic compression, and chunk‑chaining.
- Fine‑tune prompts and queries via DSPy, adapters, and advanced prompt‑engineering techniques.
- Deploy ML models with Kubeflow, Airflow, and Azure/AWS services (SageMaker, OpenAI).
- Create ETL workflows for structured and unstructured data using MongoDB, PostgreSQL, AWS Glue, Azure Data Factory.
- Manage cloud resources via Terraform, CloudFormation, Azure DevOps.
- Integrate telemetry, automated testing, and CI/CD for AI system monitoring.
- Mentor junior engineers and participate in open‑source contributions.
**Required Skills:**
- **Programming:** Python, PySpark, .NET, Django, FastAPI, TypeScript, React, NextJS.
- **AI/ML:** LangChain, PyTorch, Hugging Face, DSPy, Whisper.cpp, Granite adapters, OpenCV, OCR (pytesseract).
- **Cloud/DevOps:** AWS (SageMaker, Glue, Redshift), Azure (OpenAI, Bot Framework, LUIS), Terraform, CloudFormation, Azure DevOps.
- **Data Stores:** PostgreSQL, MongoDB, Pinecone.
- **Orchestration/Tools:** Kubeflow, Airflow, Ngrok, Streamlit.
- **Preferred:** Experience with multimodal data processing, RAG, and agentic AI systems.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
- Minimum 3 years professional experience in software engineering or AI development.
- Microsoft Azure Fundamentals (AZ‑900) certification preferred.