Senior Software Engineer, AI (New York)

Fitch Group•New York, NY

23h•$140,000 - $150,000•Hybrid

About The Position

Fitch Solutions is seeking a Senior Software Engineer, AI to join their New York office. This role is within the Technology & Data Team, a dynamic department focused on innovation and leveraging cutting-edge technologies like AI and cloud solutions. The team is recognized as a Best Place to Work in Technology. The Senior Software Engineer, AI will drive high-impact AI initiatives, identify cross-product synergies, and develop scalable, enterprise-grade solutions in a collaborative environment. The position offers the opportunity to advance your career while contributing to a company known for its expertise and commitment to excellence in financial information services.

Requirements

7+ years of professional experience designing, developing, and deploying production-grade AI/ML systems, with a proven track record of delivering sophisticated generative AI and machine learning solutions at scale.
5+ years in full stack software engineering building and deploying real-world enterprise applications.
Advanced Python programming skills with strong backend development capabilities.
Proven experience in developing and deploying intelligent conversational AI systems using RAG architectures, Model Context Protocol (MCP), AI-enabled search, vector databases, and LLM integration in real-time enterprise environments.
Hands-on enterprise experience developing GenAI applications using LangChain and LangGraph frameworks with expertise in agent architecture design, state management, memory systems, and graph-based workflow orchestration.
Strong technical expertise in embeddings, vector databases for AI integration, and LLM integration/fine-tuning across platforms such as OpenAI, LLaMA.
Solid understanding of ML algorithms with production experience using frameworks and tools including FastAPI, PyTorch/TensorFlow, and MLflow.
Extensive experience building and integrating generative AI solutions into production environments, including working with large language models, designing multi-agent systems, and embedding AI capabilities into existing product ecosystems.
Strong foundation in MLOps practices, containerization technologies (Docker, Kubernetes/AWS EKS), cloud infrastructure (AWS/Azure), AI-specific services (AWS Bedrock, SageMaker, Azure AI Search), and orchestration tools.
Excellent communication and collaboration skills, with the ability to translate complex technical concepts for diverse stakeholders and work effectively across cross-functional teams.

Responsibilities

Lead transformative GenAI system development, including enterprise chatbot platforms, evaluation frameworks, agentic workflows, RAG architectures, applied GenAI applications, and Model Context Protocol (MCP) implementations.
Deliver production-grade, scalable code that meets enterprise performance standards.
Pioneer AI innovation through hands-on engineering, experimenting with cutting-edge foundation models, designing and implementing GenAI/ML architectures, assessing emerging AI technologies, and developing rapid proofs-of-concept.
Bridge the gap between research breakthroughs and production-ready capabilities that generate tangible business value.
Build robust production infrastructure, including developing enterprise-scale APIs (FastAPI) for deployment and architecting/implementing comprehensive cloud-based AI infrastructure on AWS/Azure.
Ensure systems are optimized for performance, reliability, and scalability.
Demonstrate full-stack engineering excellence by applying proficiency across the entire technology stack to build enterprise-grade websites and applications, integrating AI capabilities seamlessly into user-facing products and backend systems.
Participate in the full software development lifecycle for AI initiatives, contribute technical insights to design discussions, and apply engineering best practices.
Collaborate with cross-functional teams, including product managers, engineers, and business stakeholders, to translate complex technical concepts, gather requirements, and ensure AI solutions address user needs and strategic objectives.