5-7 years of experience in software development, with a focus on AI systems.
Proficiency in Python or PySpark for hands-on coding and solution engineering.
Experience managing the lifecycle of machine learning models and enterprise-level AI integrations.
Strong understanding of generative AI technologies and frameworks like LLMs.
Prior experience in financial services or similar industries is preferred.
Demonstrated ability to collaborate with C-suite stakeholders and technical teams.
Familiarity with MLOps practices and Model Risk Management standards.
Responsibilities
Lead the development of production-grade AI solutions with a focus on scalability.
Design and deploy advanced generative AI pipelines for real-world applications.
Build autonomous agents to automate complex business workflows.
Manage the entire lifecycle of machine learning models, including fine-tuning and monitoring.
Act as the primary liaison for clients, translating business needs into technical specifications.
Mentor and lead a high-performing technical team in AI development.
Facilitate UAT processes to ensure solutions meet business objectives and accuracy standards.
Benefits
Opportunity to work with cutting-edge generative AI technologies.
Engagement with top-tier clients in the financial services sector.
Mentorship and leadership development opportunities.
Involvement in impactful projects that drive business value.
Dynamic and collaborative work environment with a focus on innovation.
Full Job Description
Role description
Job Role: GenAI Developer
Job Location: Irving, Texas
Job Description:
1. Engineering & Production-Grade Development (40%)
Hands-on Coding: Lead the development of production-ready systems with expert-level proficiency in Python or PySpark.
Solution Engineering: Apply advanced data structures, algorithms, and software design patterns to solve real-world financial services challenges.
Scalability & Performance: Architect high-scale solutions with a big-picture approach, ensuring system latency and infrastructure costs are optimized to drive tangible business value.
Lifecycle Management: Own the transition from MVP to live solutions, managing change control and enterprise-level AI integration.
2. Advanced AI Implementation & R&D (40%)
Generative AI & RAG: Design and deploy sophisticated RAG pipelines. Orchestrate LLM-as-a-Service (e.g., GPT-4, Gemini, Vertex AI) alongside local SLMs (e.g., Llama 3, Mixtral).
Agentic Workflows: Build autonomous agents using frameworks such as LangGraph (preferred), CrewAI, or AutoGen to automate complex, multi-step business logic.
MLOps & Governance: Take full ownership of the model lifecycle, including fine-tuning LLMs/SLMs, monitoring model drift, ground truth validation, and ensuring compliance with Model Risk Management (MRM) standards.
UAT Validation: Drive User Acceptance Testing (UAT) focused on specific business outcomes and accuracy benchmarks.
3. Leadership & Solution Orchestration (20%)
Stakeholder Management: Navigate complex client ecosystems, acting as the primary technical liaison for both C-suite stakeholders and engineering teams.
Strategic Communication: Translate high-level business requirements into sustainable, high-value generative AI solutions.
CoE Liaison: Partner with the BFS AI CoE to bring cutting-edge R&D and incubated solutions, actively participating in multiple client AI solution demos and delivery assurance to global clients.
Team Mentorship: Lead and inspire a high-performing technical team, fostering a positive, solution-oriented mind and an initiative-taking culture.
