Senior Engineer, Data and AI

This is a fully remote position, open to applicants in Maryland.

📋 Description

• This role encompasses a broad array of responsibilities, integrating both data and AI engineering.

• The individual will be tasked with the design and implementation of data infrastructure for extracting, cleaning, transferring, and storing data.

• They must possess the capability to independently create AI/ML systems and products for both internal and external applications.

• The role involves collaborating with business stakeholders to comprehend their requirements and devise solutions that meet those needs.

⛳️ Requirements

• A degree in Computer Science, Physics, Mathematics, or a related discipline; a Master's degree is advantageous.

• 3–5 years of experience in roles such as data engineer, ML engineer, AI engineer, AI infrastructure engineer, or similar positions.

• Proficiency in Python for building, training, and deploying both traditional ML models and contemporary AI applications, including LLM-based systems, RAG pipelines, and agentic workflows.

• Expertise in feature extraction/transformation and model selection, training, and evaluation.

• Familiarity with LangChain and LangGraph for developing agentic/AI workflows, as well as experience with LLM APIs like Claude and OpenAI SDKs.

• Experience in self-hosting and serving models using vLLM is considered a plus.

• Skills in building and serving APIs with FastAPI, utilizing Pydantic for data validation and schema enforcement.

• A solid understanding of statistical methods and experimental design (e.g., hypothesis testing, regression, causal inference) to validate models and support sound decision-making.

• Experience in deploying, monitoring, and maintaining models and AI systems in production, employing tools such as MLflow (for experiment tracking) and LangSmith/LangFuse (for LLM tracing and evaluation).

• Ability to discover and characterize source data systems, comprehend and model essential business concepts, and construct data models that organize data to fulfill operational and reporting needs.

• Proficiency with databases (T-SQL, NoSQL) — including writing and optimizing tables, queries, and indexes for scalability, reliability, and performance.

• Capable of designing and implementing pipelines to transfer and transform data between systems.

• Experience with data warehousing concepts and platforms such as Databricks; familiarity with Spark and Python for large-scale data processing.

• Knowledge of cloud services, particularly Azure, for scalable data storage and processing.

• Awareness of best practices regarding data quality, privacy, security, and compliance.

🏝️ Benefits

• It's enjoyable to work in an organization where individuals genuinely BELIEVE in what they are doing!

• We are dedicated to infusing passion and customer focus into our business.