Senior Artificial Intelligence/Machine Learning Engineer

As a Senior AI/ML Engineer on the Evergreen.AI team, you will design, build, and scale enterprise-grade agentic AI systems that support Evergreen’s mission to deliver production-ready intelligent solutions for Fortune 500 clients. You will lead development of advanced agent frameworks, retrieval pipelines, orchestration workflows, evaluation systems, and enterprise integrations.

This role requires deep hands-on engineering ability combined with strong architectural thinking. You will make key technical decisions, mentor junior engineers, contribute to framework design, and ensure systems meet standards for security, reliability, and operational readiness. You will collaborate closely across engineering, product, delivery, and knowledge teams to transform complex business requirements into scalable, maintainable, and secure AI solutions.

Senior engineers in Evergreen.AI demonstrate strong ownership, a solution-focused mindset, and a commitment to delivering systems that function reliably in production environments. You will also help build Evergreen.AI’s reusable frameworks, skill libraries, evaluation patterns, and knowledge foundations.

Framework and Platform Development

• Contribute heavily to Evergreen.AI’s agent frameworks, reusable skills, APIs, and microservices.
• Partner with architecture and product teams to shape roadmap priorities for tooling, patterns, and platform capabilities.

RAG, Data, and Knowledge Engineering

• Optimize RAG pipelines including chunking strategies, embedding generation, vector indexing, ranking, caching, and retrieval fusion.
• Work with ontologies, semantic models, and enterprise knowledge assets to improve grounding accuracy.

LLMOps, MLOps, and Production Deployment

• Implement production-grade CI/CD for prompts, embeddings, models, and agent runtime services.
• Define monitoring and observability patterns for hallucination rates, cost, drift, latency, and resiliency.

Cross-Team Collaboration and Delivery

• Work closely with delivery leads, product managers, and architects to translate customer requirements into scalable designs.
• Participate in client architecture sessions, technical deep dives, and solution reviews.
• Contribute to reusable accelerators, documentation, and delivery templates.

Required

• Advanced degree and 7+ or more years of experience in software engineering, applied machine learning, or backend systems with a strong AI focus.
• Hands-on experience building and deploying LLM or ML-powered applications in production environments.
• Deep understanding of agentic AI frameworks such as LangChain, Semantic Kernel, or the Assistants API.
• Strong proficiency with retrieval systems, embeddings, and vector databases such as Pinecone, Milvus, Weaviate, or Chroma.
• Expert-level Python engineering skills and experience building microservices or distributed backend systems.
• Solid experience with Azure or other major cloud platforms, including containerization and orchestration.
• Familiarity with LLMOps and MLOps tooling including CI/CD pipelines, observability, and automated evaluations.
• Strong communication skills and comfort collaborating across engineering, delivery, product, and data teams.

Preferred

• Experience fine-tuning, training, or distilling LLMs.
• Background in ontologies, taxonomies, knowledge graphs, or semantic modeling.
• Experience designing evaluation frameworks for LLM or agentic systems.
• Experience integrating AI systems with enterprise platforms such as Microsoft 365, ServiceNow, or Salesforce.
• Experience with multi-agent systems or cognitive architectures.