Senior NLP Engineer

Position Summary

We're seeking a Senior NLP Engineer with a hacker mindset to revolutionize our information extraction engine for financial/legal data processing. This is a product-driven role focused on delivering production-ready solutions where accuracy and speed of output are paramount. Our systems are customer-facing with Human-in-the-Loop (HITL) workflows, requiring optimization for seamless human-AI collaboration. You'll have complete ownership from problem analysis to production deployment, leveraging any LLM, technique, or creative approach that delivers maximum accuracy in minimum time while ensuring optimal user experience.

Key Responsibilities

Core Product-Driven AI Work

• Enhance Information Extraction Engine: Redesign and optimize our current system using state-of-the-art LLMs for financial/legal document processing with focus on production accuracy and speed
• Accuracy Optimization: Achieve highest possible extraction accuracy through any means necessary—fine-tuning, prompt engineering, ensemble methods, or hybrid approaches
• LLM Integration: Implement and experiment with various LLMs (GPT, Claude, Llama, Gemini, etc.) to find optimal solutions for production use
• Creative Problem Solving: Think like a hacker—if traditional ML doesn't work, try prompt engineering, RAG, few-shot learning, or completely novel approaches
• LLM Development: Lead the pre-training and fine-tuning of large language models (LLMs) to optimize performance for specific financial/legal use cases
• Information Retrieval, Extraction & Classification: Develop and implement techniques for retrieving, extracting, classifying, and ranking large-scale financial/legal datasets using advanced algorithms and vector databases
• Production-First Mindset: Every solution must be production-ready with measurable accuracy and performance metrics
• Time-to-Accuracy Optimization: Balance between achieving high accuracy and delivering results within acceptable time constraints

Human-in-the-Loop (HITL) Workflow Optimization

• HITL System Design: Build AI systems optimized for human review, correction, and validation workflows
• Confidence Scoring & Routing: Develop intelligent routing systems that send low- confidence extractions to human reviewers while auto-approving high-confidence results
• Interactive Correction Interfaces: Design systems that learn from human corrections and feedback in real-time
• Workflow Efficiency: Optimize human review processes to minimize time spent while maximizing accuracy improvements
• Active Learning Integration: Implement systems that strategically request human input on the most valuable data points
• Feedback Loop Optimization: Build mechanisms to continuously improve AI performance based on human corrections and preferences
• User Experience Design: Ensure seamless handoffs between AI processing and human review stages

Engineering Responsibilities

• Third-Party LLM API Integration: Seamlessly integrate and manage multiple LLM APIs (OpenAI, Anthropic, Google, etc.) with fallback mechanisms
• API Development & Management: Build robust APIs for information extraction services with proper error handling and monitoring
• System Evaluation & Benchmarking: Develop comprehensive evaluation frameworks to measure accuracy, latency, and cost across different LLM approaches
• Performance Engineering: Optimize systems for low-latency, high-throughput information extraction
• Integration Architecture: Build seamless integrations with existing financial/legal data workflows and customer systems

End-to-End Product Delivery

• Complete Product Ownership: Take problems from customer requirements through development, testing, and production deployment
• Quality Assurance: Ensure production systems maintain consistent accuracy and reliability standards
• Rapid Iteration: Quickly implement user feedback and production improvements

Startup Engineering Mindset

• Engineering-First Approach: Prioritize engineering solutions that work in production over theoretical research
• Pragmatic Decision Making: Choose solutions based on production requirements, not academic interest
• Resource Optimization: Balance accuracy, speed, and cost for optimal business outcomes
• Scalability Focus: Build systems that can handle increasing data volumes and customer demands
• Integration Expertise: Excel at connecting different systems, APIs, and data sources

Required Qualifications

Experience & Background

• 4-8 years of Applied AI/ML experience with production deployments and customer-facing products
• HITL System Experience: Hands-on experience building Human-in-the-Loop AI systems and workflows
• Production LLM Experience: Hands-on experience integrating and productionizing LLM solutions, not just research
• Information Extraction in Production: Proven track record of deploying IE systems that serve real business needs
• API Integration Expertise: Experience working with third-party APIs, handling rate limits, errors, and failovers

Technical Skills

• Programming: Expert in Python with strong software engineering practices

Production LLM Techniques

• • Pre-training and fine-tuning of large language models
  • API integration and management (OpenAI, Anthropic, Google, etc.)
  • Prompt engineering for production accuracy
  • Model evaluation and benchmarking
  • Cost optimization and performance tuning
• Information Retrieval: Vector databases, ranking algorithms, search systems
• Engineering Frameworks: FastAPI, Flask, Docker, Kubernetes, cloud services
• Quality Assurance: Multi-stage review processes, validation workflows, error detection

Critical Product Mindset

• Customer-Centric: Focus on optimizing the end-user experience in human-AI collaborative workflows
• Workflow Optimization: Understand and improve human work patterns and efficiency
• Production-First: Every solution must work reliably in customer-facing environments
• User Trust: Build systems that maintain and enhance user confidence in AI outputs
• Iterative Improvement: Design systems that get better through human interaction
• Quality Obsession: Maintain high standards for both AI accuracy and user experience

Daily activities include

• Monitoring production systems and addressing any issues
• Analyzing user feedback and correction patterns from support tickets
• Code reviews and preparing for weekly releases
• Collaborating with product and internal teams
• Incremental improvements to existing workflows
• Testing and evaluating changes through internal metrics before weekly deployment
• Planning and scoping work for upcoming weekly releases

Engineering scenarios you'll handle

• API rate limiting and failover logic when OpenAI is down
• Building custom evaluation metrics for financial/legal document accuracy
• Optimizing prompt costs while maintaining extraction quality
• Debugging production issues affecting customer workflows
• Integrating with customer systems and data formats

Success Metrics

• Production Accuracy: Measurable improvement in extraction precision/recall in live systems
• Automation Rate: Percentage of extractions that can be auto-approved without human review
• Cost Efficiency: Optimal balance of accuracy, speed, and LLM API costs
• Time to Production: Speed from problem identification to deployed solution

This Role is NOT For You If

• You prefer research over production implementation
• You want to publish papers rather than ship products
• You need perfect data or requirements before starting
• You're more interested in novel algorithms than customer problems
• You avoid the "messy" work of production systems and integrations

This Role IS Perfect For You If

• You love seeing your AI solutions used by real customers
• You get excited about optimizing production systems for accuracy and speed
• You enjoy the engineering challenges of integrating multiple LLM APIs

This Role IS Perfect For You If

• You love seeing your AI solutions used by real customers
• You get excited about optimizing production systems for accuracy and speed
• You enjoy the engineering challenges of integrating multiple LLM APIs

Who We Are

Alphastream.ai envisions a dynamic future for the financial world, where innovation is propelled by state-of-the-art AI technology and enriched by a profound understanding of credit and fixed-income research. Our mission is to empower asset managers, research firms, hedge funds, banks, and investors with smarter, faster, and curated data. We provide accurate, timely information, analytics, and tools across simple to complex financial and non-financial data, enhancing decision-making.

With a focus on bonds, loans, financials, and sustainability, we offer near real-time data via APIs and PaaS (Platform as a Service) solutions that act as the bridge between our offerings and seamless workflow integration.

To learn more about us: https://alphastream.ai/

What we offer

"At Alphastream.ai we offer a dynamic and inclusive workplace where your skills are valued and your career can flourish. Enjoy competitive compensation, a comprehensive benefits package, and opportunities for professional growth. Immerse yourself in an innovative work environment, maintain a healthy work-life balance, and contribute to a diverse and inclusive culture. Join us to work with cutting-edge technology, and be part of a team that recognizes and rewards your achievements, all while fostering a fun and engaging workplace culture."

How to Apply

Please send your resume and portfolio to hr@alphastream.ai

Disclaimer

Alphastream.ai is an equal opportunities employer. We work to provide a supportive and inclusive environment where all individuals can maximize their full potential. Our skilled and creative workforce is comprised of individuals drawn from a broad cross section of all communities in which we operate and who reflect a variety of backgrounds, talents, perspectives, and experiences. Our strong commitment to a culture of inclusion is evident through our constant focus on recruiting, developing, and advancing individuals based on their skills and talents.