Senior AI Agent Engineer / AI Systems Engineer

Hello Professionals,

Cogent Data Solutions LLC is hiring a Senior AI Agent Engineer / AI Systems Engineer for a contract role supporting an enterprise IT engagement in Annapolis, MD. This position is part of a project awarded through an existing IT services agreement with our client.

Note: This opportunity is offered solely through Cogent Data Solutions LLC, a consulting firm supporting multiple public sector IT initiatives. The end client is a government entity; however, this is not a direct hire or employment opportunity with any government agency.

Job Title: Senior AI Agent Engineer / AI Systems Engineer

Experience: 3 years

Client Name: Public-Sector Customer

Job Location: - (Hybrid - Annapolis, MD )

Bachelor of Science in Engineering, Computer Science, Data Science, or Mathematics, or a

related field (as determined by the AOC).

1.System Design & Collaboration:

Work within established constraints regarding infrastructure, programming languages, and

model selection

Contribute to technical decision-making related to data processing, retrieval strategies, and

system integration

Collaborate with team members to define agent architectures, workflows, and system design

decisions

Evaluate and select appropriate approaches for given tasks, including determining when to

use LLM-based versus non-LLM techniques

Designing and building software systems that integrate AI/ML techniques to automate tasks,

assist internal users, and improve user-facing services.

2. Testing, Evaluation, and Quality Assurance:

Assist in the design and implementation of testing and evaluation pipelines for AI/ML

systems

Develop unit and integration tests for AI-enabled workflows and data pipelines

Generate and utilize synthetic data to support evaluation and benchmarking efforts

Contribute to improving system performance, including accuracy, latency, and cost

efficiency

3. Deployment & Operations:

Support deployment of AI/ML applications within a hybrid cloud environment

Work with containerized applications to ensure reliable deployment and updates.

Optimize systems for environments with limited computational resources, including minimal

GPU availability

4. General Responsibilities:

Deliver production-grade systems aligned with defined requirements, while supporting

iterative improvement of evolving tools

Document system designs, workflows, and technical decisions as required

Stay informed on relevant advancements in AI/ML and apply them where appropriate within

project constraints

Experience with:

(1) SQL and relational database systems (e.g., PostgreSQL)

(2) Fine-tuning small language models or embedding models

(3) Contributing to or maintaining open-source software projects

(4) Graph databases or graph extensions (e.g., Neo4j, Apache AGE)

(5) Designing and implementing multi-agent or task-oriented AI systems

(6) Embedding models, vector similarity, re-ranking, and graph retrieval techniques in

RAG systems

(7) Version control systems (e.g., Git), containerization technologies (e.g., Docker), and

service-oriented architectures

(8) Collaborating with large language models (LLMs), including both API-based

integration and local deployment

(9) Validating AI-generated outputs, mitigating hallucinations, and integrating AI tools

into production service pipelines

b. Ability to:

(1) Understand data structures, algorithms, and clean coding principles

(2) Select and apply appropriate techniques (LLM and non-LLM) based on task

requirements

(3) Develop and improve testing and evaluation pipelines for AI systems, including use

of synthetic data

(4) Demonstrate proficiency in Python, including the ability to develop production-

grade backend services, APIs, middleware, and data pipelines.

(5) Design and implement AI/ML systems that operate effectively on complex,

inconsistent, or evolving datasets while balancing accuracy, latency, and cost (token

consumption)

(6) Collaborate with team members to define system architecture, agent workflows, and

data pipelines while working in constrained environments, including limited GPU

availability and predefined infrastructure

c. Knowledge of:

(1) Hybrid cloud environments and distributed system considerations

(2) Threading, asynchronous processing, and queues in backend servers

(3) React and Microsoft Teams Toolkit for developing chatbot user interfaces

(4) Non-llm data analysis techniques for structured, semi-structured, and unstructured

data

(5) Classical natural language processing (NLP) techniques in addition to LLM-based

approaches

(6) Data science and LLM-related libraries in Rust or other performance-oriented

programming languages