AI Engineer

Job Family Group / Job Family: Technology / Artificial Intelligence/ML

Role Summary

Builds and deploys production-grade AI/ML and generative AI solutions — including LLM-based applications, RAG pipelines, and agentic workflows — that solve real business problems at scale.

Key Responsibilities

•     Design, build, and productionize AI/ML models and LLM-based applications (RAG, agents, fine-tuning) for business use cases

•     Build retrieval-augmented generation pipelines including vector databases, embeddings, and evaluation harnesses

•     Implement MLOps practices: CI/CD for models, containerization, model serving, monitoring, and rollback

•     Design and manage prompt engineering, system prompts, and multi-agent orchestration (e.g., LangChain, LangGraph, LlamaIndex)

•     Optimize model performance, cost, latency, and reliability in production

•     Collaborate with data engineering and product teams to source, clean, and prepare training/evaluation data

•     Implement responsible AI practices: bias/quality evaluation, guardrails, prompt-injection defenses

•     Stay current with the fast-evolving AI/ML landscape and evaluate new tools/models for adoption

Required Qualifications & Experience

•     Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field

•     3-6 years of machine learning engineering experience, with recent hands-on experience in LLM/GenAI application development

•     Strong Python programming skills and experience with ML frameworks (PyTorch, TensorFlow)

•     Experience deploying models/services on a public cloud (AWS/Azure/GCP)

Technical Skills

•     Programming: Python (required); familiarity with async programming

•     ML/DL frameworks: PyTorch, TensorFlow, scikit-learn

•     GenAI: LLM APIs (OpenAI, Anthropic, Gemini), RAG architecture, vector databases (Pinecone, Weaviate, FAISS)

•     Orchestration frameworks: LangChain, LangGraph, LlamaIndex

•     MLOps: Docker, Kubernetes, CI/CD, model monitoring/observability

•     Cloud AI platforms: Vertex AI, Azure AI/OpenAI Service, AWS Bedrock/SageMaker

Core Competencies

•     Strong problem-solving and experimentation mindset

•     Ability to translate ambiguous business problems into technical solutions

•     Rigor in evaluation, testing, and responsible AI practices

•     Collaboration across data, product, and engineering teams

•     Curiosity and continuous learning given the fast-moving AI landscape