- Company Name
- Mavent Analytics
- Job Title
- Generative AI Engineer
- Job Description
-
**Job Title**
Generative AI Engineer
**Role Summary**
Senior AI/ML engineer operating as an independent contractor, responsible for delivering end‑to‑end machine learning pipelines and generative AI solutions at scale. Works autonomously with senior stakeholders, shipping production systems that serve real users and leveraging both traditional ML and modern LLM‑based workflows.
**Expectations**
- 10+ years overall experience in data or analytics roles, with at least 5 years shipping production ML/AI systems for large user bases.
- Proven track record collaborating with senior leadership on business‑critical initiatives.
- Ability to act as a trusted technical advisor with minimal oversight.
- Operate under a Corp‑to‑Corp agreement via own business entity (C‑Corp, S‑Corp, LLC).
- Engage in 3–6 month projects with potential for extension.
**Key Responsibilities**
1. Design, develop, and deploy end‑to‑end ML pipelines: data ingestion, feature engineering, training, evaluation, and production deployment.
2. Implement MLOps practices: experiment tracking, model registry, monitoring, and automated retraining.
3. Build and maintain cloud‑native model serving infrastructures on AWS, GCP, or Azure (including managed services).
4. Develop generative AI applications: LLM integration (OpenAI, Anthropic, Azure OpenAI, Amazon Bedrock), RAG systems, prompt engineering, and fine‑tuning.
5. Manage vector databases (Pinecone, Weaviate, Chroma) and embedding pipelines for retrieval‑augmented solutions.
6. Orchestrate AI agents using LangChain, LlamaIndex, or Semantic Kernel; design guardrails and evaluation strategies for production AI services.
7. Collaborate with data engineers, software teams, and product managers to ensure robust, scalable AI features.
**Required Skills**
*ML Engineering*
- Frameworks: TensorFlow, PyTorch, Scikit‑learn, XGBoost.
- MLOps tools: MLflow, Kubeflow, SageMaker, Databricks.
- Cloud platforms: AWS, GCP, Azure (incl. managed ML services).
- Model serving, monitoring, and retraining strategies.
*AI Engineering*
- LLM application development (OpenAI, Anthropic, Azure OpenAI, Amazon Bedrock).
- Retrieval‑Augmented Generation, prompt engineering, fine‑tuning.
- Vector databases: Pinecone, Weaviate, Chroma.
- Agent frameworks: LangChain, LlamaIndex, Semantic Kernel.
- API integration, guardrails, and production evaluation.
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, Software Engineering, Data Analytics, or related field (advanced degree preferred).
- Relevant cloud or ML certifications (e.g., AWS SageMaker, Azure AI Engineer, GCP Machine Learning) are a plus but not mandatory.
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