Machine Learning Engineer
Build and integrate AI/LLM capabilities for a customer and partner portal, including RAG, agents, retrieval systems, and evaluation pipelines. The role requires 3+ years of AI/ML engineering experience, strong Python skills, and production experience serving and evaluating LLMs.
About the job
Responsibilities
- Develop and integrate retrieval-augmented generation (RAG), AI agents, recommendation and search systems, and AI-powered automation workflows.
- Build and maintain data ingestion, embedding, retrieval, and evaluation pipelines.
- Collaborate with engineering and QA teams to design, build, test, and deliver AI features.
- Collaborate with DevOps to deploy, monitor, and optimize self-hosted large language models (LLMs).
- Establish evaluation gates for LLM changes.
- Continuously improve AI accuracy, latency, cost efficiency, security, and reliability.
Requirements
- 3+ years of relevant AI/ML engineering experience.
- Strong Python skills, including building data pipelines for training datasets.
- Understanding of prompt engineering, RAG, embeddings, vector databases, tool calling, AI agents, and LLM evaluation.
- Experience with REST APIs, SQL and NoSQL databases, Docker, Git, and CI/CD.
- Ability to debug, evaluate, and validate AI systems in production environments.
- Experience with LLM serving and APIs such as OpenAI, Anthropic, and Azure OpenAI.
- Experience with inference tools such as vLLM and Ollama, including quantization and memory, latency, and quality trade-offs on constrained hardware.
- Ability to design and run evaluations for non-deterministic systems.
- Strong verbal and written English communication skills.
- Self-motivated, adaptable, and comfortable owning work in a fast-paced, collaborative environment.
Nice to Have
- Experience fine-tuning open-weight LLMs.
- Experience with agent frameworks.
- Awareness of LLM security risks, including prompt injection, data leakage, and misuse.
- Experience deploying compact models on-premises, offline, or in resource-constrained environments.
- Experience with LLM evaluation tooling, including LLM-as-judge, regression suites, and human review loops.
- Cybersecurity knowledge or experience building security product components.
Skills
Python, RAG, AI Agents, Embeddings, Vector Databases, Prompt Engineering, Llm Evaluation, REST APIs, SQL, NoSQL, Docker, Git, CI/CD, vLLM, Ollama
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