What are the responsibilities and job description for the AI Application Engineer with Financial Domain In TX- Full time position at Smart Caliber Technology?
Job Title: AI Application Engineer with Financial Domain In TX
Employment Type: Full-Time (No C2C or Subcontractors)
Work Mode: Hybrid
Location: Westlake, TX (Dallas, TX)
Visa: No sponsorship available
12-15 yrs of IT experience
Job Description
As a GenAI Application Engineer, you will design, develop, and deploy intelligent applications leveraging advanced LLMs and generative AI technologies. You will be responsible for delivering end-to-end solutions, implementing RAG architectures, integrating diverse data sources, and building high-quality prototypes for next-generation advisor assistance tools.
Key Responsibilities
RAG Architecture & Knowledge Systems
- Design and develop RAG-based knowledge systems
- Integrate structured and unstructured data from internal and external sources
- Optimize retrieval performance, latency, and relevance using RAG evaluation metrics
Data Integration & Ingestion
- Gather and consolidate data from REST APIs, databases, enterprise systems, and document repositories
LLM-Driven Application Development
- Build applications using system prompts, agent architectures, and multimodal workflows
- Develop conversational interfaces, AI assistants, and advisor support tools
AI Engineering & Automation
- Create intelligent agents and automation to enhance engineering workflows
- Embed GenAI capabilities into existing processes to improve productivity and decision-making
Prototyping & Experimentation
- Develop production-ready prototypes and proof-of-concepts
- Iterate rapidly based on user and stakeholder feedback
DevOps, Security & Deployment
- Package and deploy applications using containers, APIs, and CI/CD pipelines
- Implement monitoring, observability, testing, and secure coding practices
- Ensure compliance with Responsible AI, privacy, and data governance standards
Required Skills
- Strong expertise in Python (minimum 2 years)
- Hands-on experience with LLMs (OpenAI, Anthropic, etc.)
- Experience with agent frameworks such as LangChain or LangGraph
- Proven experience in building RAG pipelines, including embeddings, vector search, and knowledge integration
- Minimum 6 months of experience in bot development using RAG and LLMs