AI Engineer

Bates White Economic Consulting is hiring an AI Engineer to design, develop, and deploy AI-driven workflows and autonomous agent solutions across the firm’s enterprise AI platforms. In this hybrid role in Washington, DC, you will work with stakeholders to deliver production-grade capabilities using LLMs and related AI systems, while applying responsible AI practices, evaluation, monitoring, and MLOps/LLMOps discipline.

Role overview

You will build agentic systems that leverage existing enterprise AI platforms, orchestrate multi-step workflows, and extend solutions to new modalities as needed. The role also includes platform evaluation and onboarding as requirements evolve, along with continuous improvement of deployed system performance, reliability, and cost efficiency.

Responsibilities

• Design, build, and deploy AI workflows and agentic solutions using enterprise AI platforms such as Azure AI Foundry, Azure OpenAI, Databricks, AWS (for example, Bedrock and SageMaker AI), and Google Cloud Vertex AI / Gemini, and evaluate and onboard new platforms as needs evolve.
• Develop, orchestrate, and maintain multi-step agent workflows using tool use, function calling, retrieval-augmented generation (RAG), and integrations with enterprise data sources, applications, and APIs.
• Apply strong knowledge 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.
• Partner with business stakeholders and firm leadership to convert business requirements into robust, production-grade AI solutions.
• Create reusable frameworks, components, and pipelines that help the broader data engineering team develop, test, and scale AI solutions across cloud platforms.
• Implement responsible AI practices aligned with 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, including token usage optimization.
• Build and maintain CI/CD pipelines and automated testing for AI workflows and agents, and manage the model lifecycle including version upgrades, deprecations, and migrations.
• Extend AI solutions to multi-modal use cases as needed, incorporating vision, audio, or other modalities alongside text-based LLMs.
• Stay current on the evolving AI landscape and advise the team on emerging models, tools, and techniques, including alternatives to the firm’s primary Azure-based stack (for example, AWS or Google Cloud/Gemini).

Requirements

• Bachelor’s degree in computer science, data science, engineering, or a related field (advanced degree preferred).
• Minimum 7 years of experience in software engineering, data engineering, or a closely related technical field.
• Minimum 3 years hands-on experience building AI solutions with large language models (LLMs) and agent-based systems.
• Thorough understanding of LLM architecture, capabilities, and limitations, with the ability to reason about and adopt new AI systems as they emerge.
• Proficiency in Python and modern AI/ML frameworks and libraries; experience with multi-modal models (vision, 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 with vector databases and embedding models.
• Proficiency in prompt engineering, model evaluation, and 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.
• Familiarity with Microsoft Azure and other major cloud providers (AWS, Google Cloud); the firm is Azure-focused but open to other platform experience.
• 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 is advantageous.
• Strong problem-solving and analytical skills, with the ability to work effectively under tight deadlines and high-pressure situations.
• Ability to work with individuals of varying backgrounds and levels across departments.
• Excellent oral and written communication skills for explaining complex technical and AI concepts to diverse audiences.
• May require more than 40.0 hours per week to perform essential duties.

Location and compensation

• Location: Washington, DC (hybrid)
• Salary: USD 160,000 to 190,000 per year
• Bonus: eligible for discretionary bonus compensation

Benefits

• Competitive compensation with a salary range of $160,000 to $190,000 and eligibility for discretionary bonus compensation.
• Comprehensive benefits package including tuition reimbursement up to $75K, low healthcare premiums, wellness benefits, and more.
• Hybrid work with three coordinated in-office days per week.
• Open culture with your voice heard and contributions recognized.
• Fun and engaging culture, including frequent social events.
• Amenities including a fitness center, rooftop terrace, standing desks, espresso, fresh fruit, breakfast and afternoon snack, billiards, and ping pong.
• Employee-driven community outreach with fundraising events, volunteer opportunities, and matching funds, along with a pro bono program.
• Career investment through training programs, an assigned mentor and peer coach, and frequent feedback.
• Networking opportunities through employee interest groups including Women’s Network, International Network, Diversity-Inclusion Council, and BWProud Network.

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