AI Engineer ( Full-Time Remote)

Location: DMV area, hybrid – primarily remote with in-person client engagement as needed

Travel Requirements: Occasional travel within the DC-metro area for customer engagements and team on-sites

Background Check Required: Yes

Client : Cloud AI

Position Summary

The AI Engineer builds, evaluates, and operates the generative AI and machine learning capabilities inside CloudAI's products and customer solutions. The role spans the full lifecycle: selecting and adapting models, designing retrieval and agent architectures, building the evaluation suites that show whether a system actually works, fine tuning where it earns its cost, and instrumenting what runs in production. The AI Engineer is equally responsible for the parts of the system that keep it trustworthy, including guardrails, policy enforcement, and the governance evidence customers in regulated sectors require. The role is an engineering role, and the engineer ships and operates what they build.

Key Responsibilities

Build generative AI and machine learning systems that hold up in production.

• Design and implement retrieval augmented generation, agentic, and multi-step reasoning systems, including retrieval strategy, chunking, embedding, ranking, and context management.
• Select models against the requirement rather than the trend, working across multiple frontier model families and cloud AI platforms, and design for portability between them.
• Fine tune and adapt models where evaluation shows it earns its cost, including instruction tuning, parameter-efficient methods, and the dataset construction behind them.
• Build classical machine learning components where they are the right tool, including feature engineering, training, and inference.

Prove the system works, and keep proving it.

• Build evaluation suites that measure task accuracy, groundedness, safety, and regression, including golden datasets, automated scoring, model-as-judge methods, and human review workflows.
• Instrument production systems with telemetry covering latency, cost per interaction, token consumption, tool-call success, retrieval quality, and user outcomes.
• Run experiments and comparisons, and make model, prompt, and architecture changes on evidence rather than impression.
• Establish the baseline and regression gates that prevent a change from silently degrading quality.

Keep the system safe, governed, and defensible.

• Implement guardrails, including input and output filtering, prompt injection and jailbreak mitigation, sensitive data detection and redaction, grounding and citation requirements, and refusal behavior.
• Implement policy and access controls over what a model can see and do, including tenant isolation and role-based retrieval appropriate to regulated data.
• Produce the governance artifacts customers and auditors require, including model documentation, evaluation results, data lineage, and decision logs.

Work as an engineer across the full solution.

• Ship and operate production services, including APIs, pipelines, infrastructure as code, CI/CD, monitoring, and cost management.
• Contribute to solution design and estimation, and engage customers on what is feasible, what it costs, and what it risks.
• Improve shared frameworks, evaluation harnesses, and standards as new needs surface from engagement work.

Competencies & Skills

Technical and interpersonal competencies required to succeed in this role, spanning both hard skills (tools, technologies, methodologies) and soft skills (communication, problem-solving, stakeholder management).

Core Competencies

• Customer and Business Focus: Starts with the customer, works efficiently, and delivers lasting value through continuous improvement
• Ownership and Delivery: Takes ownership, honors commitments, moves with urgency, and delivers their best work
• Curiosity and Growth: Goes deep to understand the 'why,' stays open to new ideas, and challenges the status quo
• Candor and Collaboration: Leads with transparency, welcomes thoughtful disagreement, and chooses the path based on the merit of ideas
• Empathy and Respect: Listens to understand, respects others' perspectives, and acts as a team player

Technical Competencies

• Generative AI engineering, including prompt design, retrieval augmented generation, agent and tool-use architectures, structured output, function calling, and context and memory design.
• Hands-on experience across multiple frontier model providers and multiple cloud AI platforms, including AWS Bedrock, Azure AI services, or Google Vertex AI.
• Model evaluation, including offline and online evaluation design, golden dataset construction, automated scoring, and regression testing.
• Fine tuning and model adaptation, including dataset curation, parameter-efficient fine tuning, and the judgment to know when not to fine tune.
• Machine learning fundamentals, including supervised learning, embeddings and vector search, feature engineering, and model performance analysis.
• AI safety and governance, including guardrail implementation, sensitive data handling, responsible AI frameworks such as the NIST AI Risk Management Framework, and audit evidence.
• Software and cloud engineering, including Python, API development, vector databases, containerization, infrastructure as code, observability, and cost optimization.

Delivery Competencies

• Ownership: Remains with a problem until it is resolved rather than escalating to transfer it.
• Independent judgment: Operates without supervision and determines what the work requires.
• Curiosity: Seeks to understand the customer's business, the technical solution, and the people involved rather than accepting information as given.
• Relationship building: Establishes trust with customers and delivery teams deliberately.
• Adaptability: Adjusts approach to the customer, project, and team rather than applying a fixed method.
• Evidence: Trusts measurement over intuition, and is willing to discard an approach the evaluation does not support.

Education & Experience Requirements

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
• Six (6) years of professional software or machine learning engineering experience, including at least two (2) years building generative AI systems.
• Demonstrated experience taking a generative AI system to production and operating it, including evaluation and telemetry.
• Demonstrated experience fine tuning or adapting models, with the evaluation work that justified it.
• Experience implementing guardrails, policy controls, or AI governance requirements in a regulated environment.
• Experience working across more than one model provider and more than one cloud platform.
• Experience with public sector, SLED (state, local, and education), or higher-education clients preferred, not required.
• Must physically reside in the United States and be authorized to work in the United States. U.S. citizenship or permanent residency is not required.

Certifications

• No certification required for this role.
• Cloud AI or machine learning certification preferred (e.g., AWS Certified Machine Learning, Microsoft Certified: Azure AI Engineer Associate, Google Cloud Professional Machine Learning Engineer).