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.
Our CommitmentFoundation AI is an equal opportunity employer committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic. Our hiring decisions are based solely on qualifications, merit, and business needs at the time.
For any feedback or inquiries, please contact us at [email protected]. Learn more at www.foundationai.com.
