Job OverviewWe're looking for a Senior AI/ML Engineer to help expand our next-generation document intelligence system. Working in close collaboration with our Data Science team, you'll bring deep technical rigor to a system that gets smarter with every document it digests, across hundreds of customers at scale. The system draws on a combination of ML, LLM, RAG, applied mathematics, and smart algorithm design to deliver results at a high level of accuracy.
This is a remote job.
Key Responsibilities- Retrieval-Augmented Generation: Design and build RAG architectures for document understanding, classification, and extraction - from chunking and indexing through retrieval quality and grounding.
- LLM Feature Development: Ship production LLM-powered features end-to-end, from prompt design through evaluation - not just prototypes.
- Evaluation-Driven Development: Build regression suites, confidence calibration methods, and evaluation frameworks that make AI output quality measurable.
- Collaboration with Data Science: Partner closely with our Data Science team to bring research-grade techniques into production.
- ML Pipeline & MLOps: Own model, data, and prompt versioning; build reproducible pipelines for ingestion, training, evaluation, and serving.
- Rollout Automation & A/B Testing: Implement canary deployments, side-by-side A/B testing, and rollback mechanisms for safe model and prompt releases.
- Monitoring & Observability: Implement drift detection, data quality monitoring, and alerting; define SLOs for model and pipeline health.
- System Architecture & Leadership: Design secure, high-performance ML infrastructure; evaluate tooling (Bedrock, MLflow, Airflow); mentor engineers and influence best practices.
Skills and Tools- Experience: 5+ years in software engineering, with 2-3 years focused on ML/AI in production systems.
- LLM & RAG Fundamentals: Hands-on experience with prompt engineering, RAG architectures, and evaluation-driven development - with a track record of shipping LLM-powered features real users rely on.
- MLOps & Pipeline Tooling: Practical experience with model/data/prompt versioning, experiment tracking, and deployment automation; proficiency with Airflow, MLflow, and Bedrock or equivalents.
- Programming: Proficient in Python; comfortable with SQL and data engineering patterns.
- Strongly Preferred: Working understanding of classical ML methods (gradient boosting, embeddings, calibration) sufficient to collaborate closely with Data Science; AWS infrastructure experience (S3, ECS/EKS, Lambda); familiarity with agent frameworks (LangChain, MCP) is a bonus.
EducationA B.Tech degree in Computer Science or equivalent experience relevant to the functional area.
For any feedback or inquiries, please contact us at [email protected]. Learn more at www.foundationai.com. 
