Machine Learning Researcher working with a diverse team to integrate ML techniques into fusion technology. Collaborating with scientists and engineers to drive innovative energy solutions.
About the role
- Senior Applied AI Scientist at Messagepoint developing generative AI solutions using large language models. Focused on transforming research into production systems for customer communications.
Responsibilities
- Design and deploy production multi-agent AI systems using frameworks like LangGraph, OpenAI Assistants/Responses, Swarm, CrewAI, or AutoGen to solve complex business problems at scale
- Build production RAG pipelines implementing proven retrieval techniques including hybrid search, reranking, and knowledge graph integration for enterprise-grade performance
- Engineer and optimize prompts as versioned code using established techniques (Chain-of-Thought, ReAct, self-consistency) with rigorous testing and evaluation
- Implement comprehensive evaluation and monitoring pipelines with production KPIs, quality gates in CI/CD, and automated regression testing
- Architect scalable agentic workflows with persistent state management, error recovery, and production-grade reliability
- Deploy observability and debugging infrastructure using LangSmith, TruLens, DeepEval for production monitoring and issue resolution
- Integrate safety and compliance measures including PII detection, content filtering, guardrails, and security best practices
- Optimize system performance and costs through caching strategies, model routing, batch processing, and efficient resource utilization
- Build APIs and microservices for AI systems with proper versioning, documentation, and SLA management
- Champion AI-assisted development using tools like Claude Code, Augment, and Gemini CLI to maximize engineering productivity
- Stay current with relevant AI research - Read papers from top conferences (NeurIPS, ICML, ACL) to identify techniques applicable to our use cases
- Evaluate emerging techniques - Assess new methods for potential adoption, focusing on production viability and ROI
- Implement proven research concepts - Translate well-validated research into production when it provides clear value
- Conduct targeted experiments - Test specific hypotheses about system improvements with data-driven validation
Requirements
- 5+ years of software engineering experience with at least 2 years shipping LLM-powered products to production
- MS in Computer Science or equivalent experience (PhD is a plus but not required) with strong foundation in deep learning, NLP, or machine learning
- Solid understanding of transformer architectures, attention mechanisms, and modern deep learning techniques
- Ability to read and understand research papers to stay current with the field and identify useful techniques
- Proven track record shipping LLM applications with real users, SLAs, and production monitoring
- Production deployment of multi-agent systems using frameworks like LangGraph, OpenAI Assistants, or similar
- Experience building RAG pipelines at scale with vector databases, retrieval optimization, and quality metrics
- Expert-level prompt engineering with systematic testing and optimization approaches
- Building APIs and microservices for AI systems with proper error handling and scalability
- Distributed computing and storage experience (e.g., Apache Spark, Ray, cloud storage solutions)
- Strong software engineering practices including code review, testing, CI/CD, and documentation
- Active use of AI-assisted development tools like Claude Code, Augment, Gemini CLI to maximize productivity
- Experience with production ML/AI infrastructure including monitoring, debugging, and performance optimization
- Python expertise with async programming and modern web frameworks (FastAPI, Django)
- Vector database experience (pgvector, Pinecone, Weaviate, Milvus, or FAISS)
- LLMOps/AgentOps tools familiarity (LangSmith, TruLens, DeepEval, Giskard)
- SQL and search systems experience (PostgreSQL, MySQL, Elasticsearch, Solr, OpenSearch)
- Cloud platform proficiency (AWS, GCP, or Azure) with containerization and orchestration
- API design and microservices architecture for AI systems
- Version control and collaborative development practices with Git
Benefits
- Access to latest models, robust compute infrastructure, and top-tier development tools
- Support for attending major AI conferences to stay current with the field
- Focus on sustainable engineering practices and healthy team dynamics
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