AI Engineer

Federal Home Loan Bank of Pittsburgh•Pittsburgh, PA

11d

About The Position

The AI Engineer will be responsible for designing, building, deploying, certifying, and maintaining production-grade generative AI and agentic business and technology solutions. Primary Success Factors Advance the organization’s AI strategy with a strong emphasis on identifying high- value opportunities, defining technical roadmaps, prioritizing use cases, and advocating for responsible, extensible and scalable IA adoption across the Bank. Collaborate with business and IT stakeholders to deeply understand current state processes and workflows in order to deliver AI powered solutions that deliver high-impact solutions driving measurable operational efficiency and transformation. Lead the full stack end-to-end development of custom AI solutions leveraging knowledge of MLOps, context management, and CICD practices to orchestrate AI agents and technology (e.g., Copilot AI, Snowflake AI, GitHub AI, AWS Bedrock, etc). Design, create, and implement reusable AI frameworks, prompt templates, reference architecture, and reference architectures to accelerate AI solution delivery across teams. Support the model evaluation, testing, and certification process: red-teaming, bias/fairness testing, accuracy benchmarking, hallucination detection, content safety filtering, and production readiness sign-off in regulated environments. Establish and enforce responsible AI governance practices, including aligning to the NIST AI Risk Management Framework. Collaborate with cloud, data and security architecture teams to build secure, extensible solutions Stay current with the latest capabilities in current and emerging AI agentic technology and frameworks. Experiment with alternative AI models and enhance the Bank’s overall AI technology capabilities. Provide guidance on feasibility, risks and expected business impacts of AI initiatives. Evaluate third party application’s AI capabilities to ensure the usage is aligned with the Bank’s risk appetite. Draft clear documentation for AI models, data flows, AI solutions, APIs and operational procedures.

Requirements

10 years of overall experience in software engineering, data science, or related technical roles, with strong proficiency in coding, business and system design, and production deployment.
A minimum of 1-2 years of hands-on experience building and deploying generative AI solutions in enterprise environments
Bachelor’s degree in Artificial Intelligence, Data Science, or related field or an equivalent combination of education and work experience
Proficient in backend development using AI algorithms. Python, Java, Shell, Linux or Node.js, MCP, APIs, knowledge graphs, vector databases, & data structures
Demonstrated knowledge of MLOps, model lifecycle, and (CI/CD) practices.
Designed and implemented full-stack AI solutions using modern MLOps and AI frameworks (e.g RAG) and platforms such as GitHub Agent, Snowflake agents, Copilot Studio and AWS Bedrock.
Created reusable frameworks, prompt templates, and reference architectures to accelerate AI solution delivery across teams.
Knowledge of data query, analysis, and scanning tools and techniques
Strong interpersonal, written and oral communication, and analytical skills
Ability to manage multiple priorities, work independently, coordinate work assignments with management throughout the organization and reliably meet commitments
Strong aptitude for technology and an ability to learn quickly
Candidates with 7+ years of overall experience in software engineering, data science, or related technical roles, with strong proficiency in coding, business and system design, and production deployment as well as 1 year of hands-on experience building and deploying generative AI solutions will be considered for an alternative role.

Nice To Haves

Industry certification or eligibility preferred
Knowledge of Bank business applications is a plus

Responsibilities

Designing, building, deploying, certifying, and maintaining production-grade generative AI and agentic business and technology solutions.
Advance the organization’s AI strategy with a strong emphasis on identifying high- value opportunities, defining technical roadmaps, prioritizing use cases, and advocating for responsible, extensible and scalable IA adoption across the Bank.
Collaborate with business and IT stakeholders to deeply understand current state processes and workflows in order to deliver AI powered solutions that deliver high-impact solutions driving measurable operational efficiency and transformation.
Lead the full stack end-to-end development of custom AI solutions leveraging knowledge of MLOps, context management, and CICD practices to orchestrate AI agents and technology (e.g., Copilot AI, Snowflake AI, GitHub AI, AWS Bedrock, etc).
Design, create, and implement reusable AI frameworks, prompt templates, reference architecture, and reference architectures to accelerate AI solution delivery across teams.
Support the model evaluation, testing, and certification process: red-teaming, bias/fairness testing, accuracy benchmarking, hallucination detection, content safety filtering, and production readiness sign-off in regulated environments.
Establish and enforce responsible AI governance practices, including aligning to the NIST AI Risk Management Framework.
Collaborate with cloud, data and security architecture teams to build secure, extensible solutions
Stay current with the latest capabilities in current and emerging AI agentic technology and frameworks.
Experiment with alternative AI models and enhance the Bank’s overall AI technology capabilities.
Provide guidance on feasibility, risks and expected business impacts of AI initiatives.
Evaluate third party application’s AI capabilities to ensure the usage is aligned with the Bank’s risk appetite.
Draft clear documentation for AI models, data flows, AI solutions, APIs and operational procedures.