Senior AI Engineer

Accountabilities:: Design, build, and deploy generative AI capabilities across products, with a key focus on research and conversational AI experiences. Develop AI applications using large language models, retrieval-augmented generation, vector search, tool use, and agentic systems. Build reliable services and APIs that enable product teams to integrate AI capabilities into customer-facing experiences. Transform prototypes and experiments into production-ready systems with measurable performance and quality standards. Explore new AI-driven approaches for helping customers collect, understand, and act on information. Design and operate machine learning services and workflows using Python, Docker, Kubernetes, and AWS. Build reliable batch and real-time processing pipelines using technologies such as Kafka and Airflow. Design and implement vector database solutions supporting retrieval, recommendations, personalization, and semantic search. Use MLflow to manage experiments, model versions, registries, and deployments. Improve the reliability, scalability, performance, and cost efficiency of AI systems operating in production. Build automated evaluation pipelines for generative AI applications and conversational or analytical AI capabilities. Develop benchmarks covering accuracy, relevance, reliability, fairness, latency, and cost. Evaluate retrieval approaches, including chunking, embeddings, context selection, and reranking. Monitor production AI systems, identify quality and performance gaps, and implement improvements. Create safeguards to reduce unexpected AI behavior and help protect customer data. Establish reusable engineering patterns and technical standards for building, evaluating, deploying, and monitoring AI systems. Help teams make informed decisions regarding models, frameworks, infrastructure, performance, scalability, and cost. Apply strong engineering practices across testing, security, observability, version control, and deployment. Share technical knowledge and contribute to the growth and development of other engineers. Stay current with relevant AI research, tools, and engineering practices and apply valuable developments where appropriate. Partner with Product, Engineering, Data Science, Data Engineering, and Analytics teams to align AI initiatives with customer and business needs. Work with Data Scientists to turn experiments and models into reliable production services. Clearly communicate technical concepts, risks, and tradeoffs to both technical and nontechnical stakeholders. Contribute to technical planning and help shape the broader direction of AI engineering. Requirements At least 4 years of experience building and deploying machine learning or AI systems in production. Strong Python programming and software engineering skills. Experience building production services with Python frameworks such as FastAPI. Hands-on experience developing generative AI applications using large language models, RAG, tool use, or agentic systems. Experience with frameworks such as PyTorch, LangChain, LangGraph, or comparable technologies. Strong understanding of enterprise RAG systems, including chunking, embeddings, retrieval, reranking, evaluation, and monitoring. Experience creating automated evaluation frameworks and pipelines for generative AI applications. Experience with AWS, Docker, Kubernetes, Terraform, and CI/CD practices. Experience with AWS services such as SageMaker or Bedrock. Experience with Kafka, vector databases, or similar technologies used for real-time and high-dimensional data processing. Experience managing machine learning workflows with MLflow. Experience monitoring production systems using tools such as Datadog or OpenSearch. Ability to balance quality, speed, reliability, scalability, and cost when making technical decisions. Strong communication skills and proven ability to collaborate across Product, Engineering, and Data teams. Experience in a B2B SaaS product environment is a plus. Familiarity with orchestration tools such as Airflow or Argo Workflows is a plus. Familiarity with SQL, Spark, Snowflake, or other data-processing technologies is a plus. Experience combining structured data, unstructured data, and generative AI is a plus. Knowledge of AI security, privacy, responsible AI, prompt-injection protection, or data-leakage prevention is a plus. Experience improving latency and cost efficiency for AI systems operating at scale is a plus. Benefits Fully remote work arrangement. Opportunity to work from the US Eastern Time zone. Hands-on ownership across the full AI product lifecycle, from experimentation to production. Opportunity to work with generative AI, LLMs, RAG, vector search, agentic systems, and machine learning infrastructure. High-impact work focused on improving how customers collect, understand, and act on information. Close collaboration with Product, Engineering, Data Science, Data Engineering, and Analytics teams. Opportunity to influence technical standards and the broader AI engineering practice. Inclusive, collaborative environment grounded in respect, transparency, trust, and diverse perspectives. Equal-opportunity workplace committed to preventing discrimination and harassment.