- Company Name
- PlanHub
- Job Title
- Senior AI Engineer
- Job Description
-
**Job Title:** Senior AI Engineer (Generative AI)
**Role Summary:**
Lead the design, development, and deployment of production‑grade generative AI and RAG solutions. Bridge product vision with engineering execution, building AI features that customers use daily. Architect agentic workflows, ensure responsible AI practices, and integrate AWS‑native tools and modern AI frameworks.
**Expectations:**
- Translate product and customer needs into high‑impact AI initiatives.
- Own full delivery cycle from rapid prototyping to production rollout.
- Maintain rigorous quality, safety, cost, and performance standards.
- Champion responsible AI: data provenance, bias mitigation, hallucination reduction, explainability, guardrails.
- Collaborate with cross‑functional teams (Product, Engineering, Support, etc.) to validate fit and refine UX.
- Continuously evolve CI/CD, MLOps, and AI/ML benchmarking systems.
**Key Responsibilities:**
- Identify and scope AI opportunities aligned with product roadmap.
- Build prototypes and PoCs to validate concepts and shape product direction.
- Develop MVPs and fully production‑grade AI solutions using LLMs, multimodal models, agentic workflows, and RAG pipelines.
- Architect, deploy, and optimize agentic AI solutions on AWS, focusing on accuracy, safety, performance, and cost.
- Work with data engineering to improve pipelines, metadata quality, and data read‑iness for RAG and model training.
- Evaluate and integrate third‑party AI services, frameworks (LangChain, Haystack, Bedrock, etc.) and tooling.
- Contribute to AI CI/CD, MLOps/AIOps: automated evaluation, drift detection, testing frameworks.
- Apply and enforce responsible AI principles across all deliverables.
**Required Skills:**
- 7+ years Python development (FastAPI or equivalent).
- 3+ years experience with CI/CD (containers, microservices) for AI workloads.
- Hands‑on RAG (HYDE, semantic search, chunking, embeddings, re‑ranking).
- Experience with multimodal LLMs and evaluation strategies.
- 1+ year building agentic AI workflows (Bedrock Agents, LangChain/LangGraph, CrewAI, AutoGen, etc.).
- Familiarity with AWS AI/ML services (Bedrock, SageMaker, Redshift, Aurora).
- Strong understanding of MLOps practices: model monitoring, drift detection, automated testing.
- Knowledge of responsible AI: data quality, bias, hallucination mitigation, explainability, guardrails.
- Excellent problem‑solving, documentation, and communication skills.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Mathematics, or related field.
- AWS Certified Machine Learning – Specialty or equivalent ML certifications preferred.
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