Job Title- AI Analytics Engineer
Location – Austin, TX (Onsite - 5 DAY/WEEK)
Employment Type – Fulltime
Are you the right applicant for this opportunity Find out by reading through the role overview below.
Role Overview
Key Responsibilities
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Architect and develop end-to-end AI/LLM solutions using LangChain and modern frameworks
• Design and implement RAG-based systems for financial data processing and insight generation
• Build and orchestrate multi-step AI workflows and autonomous agents using LangGraph or similar tools
• Develop AI-driven automation pipelines to replace manual financial and data processes
• Write efficient, scalable Python code for AI workflows, data processing, and integrations
• Design and optimize complex SQL queries for large-scale data extraction, validation, and transformation
• Integrate AI systems with enterprise platforms, APIs, and data warehouses
• Develop evaluation frameworks for model accuracy, performance, and compliance
• Implement hallucination detection, monitoring, and output validation strategies
• Collaborate with business and technical stakeholders to identify AI-driven transformation opportunities
• Ensure scalability, security, and performance of deployed AI systems
Core Requirements (Must-Have)
1. LangChain & LLM Expertise
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Strong hands-on experience with LangChain and LLM ecosystems
Ability to: Build complete LLM pipelines (ingestion → processing → output)
Manage chains, agents, tools, and memory
Deliver production-grade AI applications
2. Python Development (Critical)
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Strong proficiency in Python programming
Experience in: Building AI/ML pipelines and backend services
Data processing using libraries (Pandas, NumPy, etc.)
API development (FastAPI/Flask)
Writing clean, scalable, and production-ready code
3. RAG (Retrieval-Augmented Generation) Development
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Proven experience building RAG-based AI systems
Strong understanding of: Data ingestion and chunking strategies
Embeddings and vector databases
Context orchestration (retrieve → reason → respond)
4. Advanced SQL & Data Engineering
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Strong expertise in SQL (mandatory)
• Ability to:
• Write complex queries (joins, window functions, aggregations)
• xsgimln Perform data validation and reconciliation
• Work with large-scale financial datasets
• Experience with: Data pipelines and ETL/ELT processes
• Data modeling and schema design
5. Workflow Orchestration & Autonomous Agents
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Experience with LangGraph or similar frameworks
Ability to: Design multi-step reasoning workflows
Orchestrate LLMs, APIs, and tools in decision pipelines
6. AI-Driven Problem Solving (Financial Domain Focus)
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Ability to transform financial workflows using AI-driven automation
Experience in: Source-to-target data mapping automation
Metadata extraction (e.g., information schema)
Semantic matching and fuzzy logic
Leveraging logs and historical data for intelligent outputs