AI Engineer

DataJobs; Washington (District of Columbia); Hybrid; USD 160,000 - 190,000; Full time

Benefits offered by this job

Hybrid work environment

Tuition reimbursement or training stip

Career development support

Job summary

Bates White Economic Consulting is seeking an AI Engineer to design, develop, and deploy AI-driven workflows and autonomous agents using LLMs and other AI systems across the firm's enterprise AI platforms.

You will design, build, and deploy AI workflows leveraging Azure AI Foundry, Azure OpenAI, Databricks, Bedrock, SageMaker AI, Vertex AI, Gemini, and more, with emphasis on scalable pipelines and governance.

Qualifications

• Bachelor’s degree in computer science, data science, engineering, or related field.
• 7+ years in software/data engineering or related technical field.
• 3+ years building AI solutions with LLMs and agent-based systems.
• Proficient in Python and modern AI/ML frameworks.

Responsibilities

• Design, build, and deploy AI workflows and agentic solutions using LLMs across enterprise platforms.
• Develop multi-step agent workflows with tool use, function calling, and retrieval-augmented generation.
• Apply understanding of LLMs, prompt design, embeddings, and vector stores for use cases.
• Partner with stakeholders to translate requirements into production AI solutions.
• Build reusable frameworks and pipelines to scale AI across cloud platforms.
• Establish responsible AI practices including data privacy, prompt safety, and governance.
• Monitor and improve accuracy, performance, cost, and reliability of AI systems.
• Maintain CI/CD pipelines and manage model lifecycles and migrations.

Skills

LLM workflows

Python

REST APIs

MLOps/LLMOps

Cloud platforms

Communication

Team collaboration

Education

Bachelor’s degree in CS/data science/engineering

Tools

LangChain

LlamaIndex

Semantic Kernel

MLflow

Weights & Biases

LangSmith

Job description

Bates White Economic Consulting is seeking an AI Engineer to design, develop, and deploy AI-driven workflows and autonomous agents using LLMs and other AI systems across the firm's enterprise AI platforms.

Key Responsibilities

• Design, build, and deploy AI workflows and agentic solutions that leverage LLMs and other AI systems, using existing enterprise AI platforms (including Azure AI Foundry, Azure OpenAI, Databricks, AWS such as Bedrock and SageMaker AI, and Google Cloud Vertex AI and Gemini), and evaluate and onboard new platforms as needs evolve.
• Develop, orchestrate, and maintain multi-step agent workflows, including tool use, function calling, retrieval-augmented generation (RAG), and integration with enterprise data sources, applications, and APIs.
• Apply a thorough understanding of LLMs and emerging AI systems, including model selection, prompt design, context management, embeddings and vector stores, and tradeoffs among inference, retrieval, and fine-tuning to choose appropriate approaches for each use case.
• Partner with business stakeholders and firm leadership to translate business requirements into production-grade AI solutions.
• Build reusable frameworks, components, and pipelines that allow the broader data engineering team to develop, test, and scale AI solutions efficiently across cloud platforms.
• Establish and apply responsible AI practices aligned with firm AI governance policies and client contractual obligations, including data privacy and security, prompt injection and abuse mitigation, and evaluation and guardrails.
• Monitor, evaluate, and continuously improve accuracy, performance, cost, and reliability of deployed AI systems, including token usage optimization.
• Build and maintain CI/CD pipelines and automated testing for AI workflows and agents, and manage model lifecycle activities such as version upgrades, deprecations, and migrations as providers release new models.
• Extend AI solutions to multi-modal use cases as needed by incorporating vision, audio, or other modalities alongside text-based LLMs.
• Stay current with the evolving AI landscape and advise the team on emerging models, tools, and techniques, including alternatives to the firm's primary Azure-based stack such as AWS and Google Cloud/Gemini.

Required Qualifications

• Bachelor’s degree in computer science, data science, engineering, or a related field (advanced degree preferred).
• Minimum 7 years’ experience in software engineering, data engineering, or a closely related technical field.
• Minimum 3 years of hands-on experience building AI solutions focused on large language models (LLMs) and agent-based systems.
• Thorough understanding of LLM architecture, capabilities, and limitations, including the ability to reason about and adopt new AI systems as they emerge.
• Proficiency in Python and modern AI/ML frameworks and libraries; familiarity with multi-modal models (vision and audio) is a plus.
• Demonstrated experience building agentic workflows, RAG pipelines, and LLM integrations using frameworks such as LangChain, LlamaIndex, Semantic Kernel, or comparable tooling.
• Experience with enterprise AI platforms including Azure AI Foundry, Azure OpenAI, or Databricks, or comparable platforms on AWS (Bedrock, SageMaker AI) or Google Cloud (Vertex AI, Gemini).
• Experience working with vector databases and embedding models.
• Proficiency in prompt engineering, model evaluation, and the use of guardrail and safety tooling.
• Proficiency working with REST APIs and integrating AI systems into enterprise applications and data pipelines.
• Familiarity with MLOps/LLMOps tooling such as MLflow, Weights & Biases, and LangSmith for experiment tracking and model monitoring.
• Familiarity with Microsoft Azure and other major cloud providers (AWS, Google Cloud); the firm is Azure-focused but open to other platforms.
• Familiarity with data privacy, security, and responsible-AI considerations for enterprise AI systems is preferred.
• Experience with SQL and working with structured and unstructured data sources is advantageous.
• Strong problem-solving and analytical skills.
• Ability to work effectively under dynamic circumstances, tight deadlines, and high-pressure situations.
• Ability to successfully work with individuals of varying backgrounds, levels, and departments.
• Excellent oral and written communication skills, including communicating complex technical and AI concepts to diverse audiences.
• May require more than 40.0 hours per week to perform the essential duties of the position.

Technologies

• Azure AI Foundry, Azure OpenAI, Databricks
• AWS: Bedrock, SageMaker AI
• Google Cloud: Vertex AI, Gemini
• Python, LLMs, RAG
• LangChain, LlamaIndex, Semantic Kernel
• Vector databases, embeddings
• CI/CD pipelines, REST APIs
• MLflow, Weights & Biases, LangSmith
• Microsoft Azure, SQL

Compensation and Benefits

• Competitive compensation: USD 160,000 to 190,000 per year; eligible for bonus compensation on a discretionary basis.
• Comprehensive benefits package: tuition reimbursement up to $75K, low healthcare premiums, wellness benefits, and more.
• Hybrid work environment: three coordinated in-office days per week.
• Culture: open culture with emphasis on your voice, input, and recognition for contributions; fun and engaging culture with frequent social events.
• Amenities: fitness center, rooftop terrace, standing desks, espresso, fresh fruit, breakfast and afternoon snack, billiards, and ping pong.
• Community outreach: employee-driven community outreach featuring fundraising events, volunteer opportunities, and matching funds along with the pro bono program.
• Career support: training programs, an assigned mentor and peer coach, and frequent feedback.
• Networking opportunities: employee interest groups, Women's Network, International Network, Diversity-Inclusion Council, and BWProud Network.

Location

Washington, DC (hybrid).