AI / LLM Engineer

Role: AI / LLM Engineer

Location: McLean, VA (Locals Only)

Visa:

Required Qualifications

• 3 5 years of software engineering experience, with at least 1 2 years building with LLMs or applied ML in a production or near-production setting.
• Strong proficiency in Python (or comparable) and solid engineering fundamentals testing, version control, clean and maintainable code.
• Hands-on experience building RAG systems: embeddings, vector databases, and chunking/indexing strategies, with a real sense of how to diagnose and improve retrieval quality.
• Experience building data ingestion/ETL pipelines and working with large, messy, realworld datasets.
• Experience integrating third-party APIs into backend services, including authentication flows (OAuth/SSO) and webhook/event-driven patterns.
• Hands-on experience with LLM APIs (e.g., Anthropic, OpenAI, or similar) and at least one orchestration framework.
• Experience evaluating model and retrieval outputs building eval sets, measuring quality, iterating.
• A careful approach to data access, permissions, and handling sensitive information.
• Familiarity with at least one major cloud platform (AWS preferred). Preferred Qualifications
• Experience with Amazon Bedrock or other managed LLM platforms.
• Experience integrating with enterprise collaboration platforms (chat, wikis, ticketing) via their APIs.
• Experience with knowledge graphs or entity-relationship modeling for retrieval.
• Experience building multi-user or multi-tenant systems with scoped permissions and audit requirements.
• Familiarity with observability/tracing for LLM or data pipelines.
• Basic Lops exposure: Docker, CI/CD, deploying services to production.
• Bachelor s degree in Computer Science, Engineering, or a related field or equivalent practical experience.

Role: AI Engineer

Location: McLean, VA(Locals Only)

Visa:

Visa:

• 10+ years in applied machine learning / data science, with deep hands-on experience in recommender systems, learning-to-rank, or large-scale personalization.
• Practical experience building with LLMs in production: generating and integrating model derived features or profiles, working with embeddings, and reasoning about evaluation, latency, and cost.
• Experience with Amazon Bedrock or comparable managed LLM platforms for production inference.
• Hands-on experience with segment- or cohort-based personalization, including measuring performance at the segment level rather than relying on aggregate metrics.
• Experience designing cold-start strategies for users or items with limited history.
• Strong communication skills able to explain modeling decisions, trade-offs, and results clearly to engineers, data scientists, and senior business stakeholders, and to manage expectations through ambiguity.
• Customer-facing or stakeholder-facing experience: building trust, navigating competing priorities, and serving as a senior technical voice in high-stakes conversations.
• A track record of technical leadership through mentoring engineers, driving design decisions, and setting standards.
• Strong track record taking ML models from experimentation to production, owning the offline-to-online validation story (ranking metrics, ablations, segment analysis, shadow testing, A/B readiness). Deep, hands-on expertise in deep learning for ranking/recommendation multi-task learning, embedding-based architectures with a major framework (TensorFlow or PyTorch).
• Strong feature engineering on large behavioral datasets using the modern data stack (PySpark, SQL, distributed data lakes).
• Rigorous experimental methodology hyperparameter optimization, bias correction, and a disciplined, hypothesis-driven approach to measuring true lift.
• Hands-on AWS experience across the ML lifecycle, and strong proficiency in Python. Preferred Qualifications
• Experience personalizing ranking for marketplaces or consumer platforms at scale (ecommerce, food delivery, media, or similar).
• MLOps maturity: model versioning, monitoring, and reproducible training pipelines.
• Advanced degree in Computer Science, Machine Learning, Statistics, or a related quantitative field.
• Prior experience in a client-facing consulting or professional-services delivery environment