Note: The job is a remote job and is open to candidates in USA. SQOR.ai is an AI-native Decision Intelligence platform that transforms business data into real-time, actionable insights. The Senior ML & AI Systems Engineer will refine and scale the machine learning and NLP systems that power the platform, directly influencing its intelligence and performance.
Responsibilities
• Optimize ML & NLP Systems for Decision Intelligence
• Refine and deploy ML pipelines for accuracy, adaptability, and speed.
• Improve model performance, context handling, and retrieval mechanisms.
• Enhance inference efficiency using fine-tuning, quantization, and caching techniques.
• Collaborate closely with the Chief Data & AI Officer on continuous optimization and performance tracking.
• Evolve NL2SQL & Query Intelligence
• Design and optimize natural language to SQL (NL2SQL) translation systems for dynamic query generation.
• Advance contextual query understanding across structured, semi-structured, and federated data.
• Develop schema-aware embedding and retrieval strategies that improve result precision and interpretability.
• Advance Agentic AI & Multi-Agent Architectures
• Design and improve reasoning loops, context injection, and learning behaviors in multi-agent systems.
• Implement adaptive frameworks that enhance collaboration and task orchestration among agents.
• Work cross-functionally to ensure stability, performance, and adaptability in agent-driven analytics.
• System Performance & Scalability
• Reduce latency and optimize system throughput for high-volume inference workloads.
• Implement distributed inference and load-balancing strategies for production scale.
• Collaborate with infrastructure engineers to ensure robustness, monitoring, and observability across ML and NLP pipelines.
Skills
• 7+ years of experience designing and optimizing ML and NLP systems in production environments.
• Deep understanding of causal inference, predictive modeling, and time-series analysis.
• Proven expertise in fine-tuning and deploying large language models (LLMs) efficiently.
• Advanced proficiency in Python and frameworks such as LangChain, LangGraph, or AutoGen.
• Experience implementing RAG pipelines, NL2SQL systems, and semantic query interpreters.
• Familiarity with vector databases (Pinecone, Weaviate, or Vertex AI Matching Engine).
• Strong grounding in microservice architectures, asynchronous messaging, and orchestration (GCP, Pub/Sub, Kubernetes).
• Experience building or optimizing conversational or decision-oriented AI systems.
• Familiarity with BI, analytics automation, or Decision Intelligence platforms.
• Knowledge of model evaluation frameworks and reinforcement learning from human or system feedback (RLHF/RLAIF).
• Experience with embedding optimization, semantic search, or hybrid retrieval pipelines.
• Strong understanding of LLM performance trade-offs and multi-model routing.
Benefits
• Approximately 1% to 2% of company equity
• Competitive equity, long-term upside
Company Overview
• At SQOR.ai, we’ve created the ultimate solution for D&A and business teams to harness real-time, actionable insights across their entire organization—without the cost, complexity, or guesswork of traditional BI infrastructure. It was founded in 2021, and is headquartered in New York, New York, USA, with a workforce of 11-50 employees. Its website is https://www.sqor.ai.