AI/ML Software Engineer || W2/C2C Contract || Remote

[Leo Weaver]

Hello

Hope you are doing great today. This is Leo Weaver from E-Solutions Inc.

 

Please review the job description if you find yourself comfortable with the description. Please share with me your updated resume along with the best time to reach you a quick discussion! 

Copy of their proposed resource (s) Education degree or other certificates evidence

Role:  AI/ML Software Engineer

Location: Remote

Job Type: W2/C2C Contract

Job  Description:

OFFEROR RESOURCE QUALIFICATIONS

 

1.                  Bachelor of Science in Engineering, Computer Science, Data Science, or Mathematics, or a related field (as determined by the AOC).

2.                  At least three (3) years’ experience in data science, machine learning, or applied AI development.

3.                  At least three (3) years’ experience in software engineering, architecture, or web development.

 

SCOPE OF WORK

Offeror proposed resource(s) shall be responsible for the following:

 

1.                  System Design & Collaboration:

a. Work within established constraints regarding infrastructure, programming languages, and model selection

b. Contribute to technical decision-making related to data processing, retrieval strategies, and system integration

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

D. Evaluate and select appropriate approaches for given tasks, including determining when to use LLM-based versus non-LLM techniques

E. 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:

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

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

C. Generate and utilize synthetic data to support evaluation and benchmarking efforts

D. Contribute to improving system performance, including accuracy, latency, and cost efficiency.

3.                  Deployment & Operations:

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

B. Work with containerized applications to ensure reliable deployment and updates.

C. Optimize systems for environments with limited computational resources, including minimal GPU availability

4.                  General Responsibilities:

A. Deliver production-grade systems aligned with defined requirements, while supporting iterative improvement of evolving tools

B. Document system designs, workflows, and technical decisions as required

C. Stay informed on relevant advancements in AI/ML and apply them where appropriate within project constraints

 

RESOURCE(S) SKILLS, EXPERIENCE, & CAPABILITIES

Experience with:

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

• Fine-tuning small language models or embedding models

• Contributing to or maintaining open-source software projects

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

• Designing and implementing multi-agent or task-oriented AI systems

• Embedding models, vector similarity, re-ranking, and graph retrieval techniques in RAG systems

• Version control systems (e.g., Git), containerization technologies (e.g., Docker), and service-oriented architectures

• Collaborating with large language models (LLMs), including both API-based integration and local deployment

• Validating AI-generated outputs, mitigating hallucinations, and integrating AI tools into production service pipelines

 

Ability to:

• Understand data structures, algorithms, and clean coding principles

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

• Develop and improve testing and evaluation pipelines for AI systems, including use of synthetic data

• Demonstrate proficiency in Python, including the ability to develop production grade backend services, APIs, middleware, and data pipelines.

• Design and implement AI/ML systems that operate effectively on complex, inconsistent, or evolving datasets while balancing accuracy, latency, and cost (token consumption)

• 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

 

Knowledge of:

• Hybrid cloud environments and distributed system considerations

• Threading, asynchronous processing, and queues in backend servers

• React and Microsoft Teams Toolkit for developing chatbot user interfaces 

• Non-llm data analysis techniques for structured, semi-structured, and unstructured data

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

• Data science and LLM-related libraries in Rust or other performance-oriented programming languages