Job Title- AI Analytics Engineer

Location – Austin, TX (Onsite - 5 DAY/WEEK)

Employment Type – Fulltime

Role Overview

Key Responsibilities

• 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

• 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)

• 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

• 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

• Strong expertise in SQL (mandatory)

• Ability to:

• Write complex queries (joins, window functions, aggregations)

• 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

• 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)

• 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