Lead Software and AI Engineer

Job description

A leading investment organization is seeking a hands-on Lead Software and AI Engineer to design and deliver modern applications and production-grade artificial intelligence solutions.

The position combines application development, software architecture, data engineering, and applied AI. The engineer will remain close to the code while providing technical direction across complex implementation efforts. Key areas include scalable application architecture, APIs and distributed systems, cloud technologies, large language models, retrieval-augmented generation, agentic AI, and data-intensive applications.

The ideal candidate has significant experience taking sophisticated software and AI solutions from initial design through reliable production deployment.

Responsibilities

• Design and build scalable applications and distributed technology solutions, contributing production code throughout the development lifecycle.
• Lead technical design and implementation for application, data, and AI initiatives.
• Translate business and investment needs into maintainable application and data architectures.
• Develop APIs, microservices, data pipelines, transformations, validation processes, application components, and automated tests.
• Build production AI applications using large language models, retrieval-augmented generation, and agentic workflows.
• Develop autonomous and human-in-the-loop AI systems incorporating capabilities such as planning, tool use, memory, and multi-step reasoning.
• Create document-processing solutions covering ingestion, OCR, parsing, normalization, extraction, validation, and structured output.
• Integrate AI capabilities with proprietary business and investment data.
• Apply AI-assisted engineering techniques across coding, testing, validation, and code review.
• Design solutions that address data quality, modeling, structure, and governance as part of the overall application architecture.
• Work across application development, data, infrastructure, security, DevOps, and quantitative technology disciplines to deliver integrated solutions.
• Troubleshoot complex engineering problems and help establish practical standards for software, data, and responsible AI development.
• Deliver solutions from technical design through deployment, operation, and ongoing maintenance.

Required experience and skills

Application and software engineering

• Extensive hands-on software engineering and application delivery experience, including designing and implementing large-scale systems.
• Strong architecture skills combined with the ability to write production code regularly.
• Proficiency with Python or Java, with Python preferred.
• Full-stack development experience using TypeScript and Node.
• Experience designing scalable microservices using FastAPI and technologies such as GraphQL and gRPC.
• Strong knowledge of modern application architecture, distributed systems, automated testing, and CI/CD.
• Experience containerizing and deploying applications with Docker and Kubernetes, including AKS or EKS.
• Experience deploying production applications to AWS, Azure, or GCP.
• Experience with real-time or streaming platforms such as Apache Kafka or Azure Event Hubs.

Data engineering

• Strong experience with data modeling, ETL/ELT, data quality, and analytics engineering.
• Experience with Snowflake or comparable cloud data warehouses.
• Ability to incorporate data quality, structure, and governance into application and platform design.
• Experience developing pipelines, transformations, application views, and automated data-validation processes.

AI and machine learning engineering

• Demonstrated experience moving generative AI solutions from experimentation into reliable production systems.
• Hands-on experience designing and deploying LLM applications, RAG architectures, and agentic AI workflows.
• Experience with large language model platforms such as OpenAI, Anthropic, or open-source models.
• Experience developing tool-enabled agentic systems using Model Context Protocol (MCP) and frameworks such as FastMCP.
• Experience designing autonomous or multi-agent systems incorporating task planning, memory, tool use, multi-step reasoning, and human oversight.
• Familiarity with orchestration frameworks such as LangGraph, AutoGen, or CrewAI, or comparable technologies.
• Experience designing document-ingestion architectures for PDF, Word, Excel, and scanned content, including OCR, layout-aware parsing, extraction, and normalization.
• Hands-on experience with document-processing technologies such as Azure Document Intelligence, LlamaParse, Tesseract, Docling, PyMuPDF, Camelot, or Tabula.
• Experience creating custom data-extraction logic and integrating extraction, validation, and structuring into AI-driven workflows.
• Experience fine-tuning language models for domain-specific extraction use cases.
• Experience applying AI to software development using tools such as GitHub Copilot, Codex, or Claude Code.

Additional relevant experience

• Strong technical design, analytical, organizational, and problem-solving abilities.
• Ability to collaborate effectively across engineering and business disciplines and provide technical direction without relying on formal management authority.
• Experience delivering complex software projects through production deployment.
• Experience with DBT, Dagster, Airflow, or Azure data tooling is valuable.
• Exposure to Azure OpenAI, Azure AI Foundry, or comparable cloud AI platforms is valuable.
• Knowledge of financial markets, investment systems, private equity, or private credit is valuable.
• Experience with probabilistic or simulation-based modeling, including Monte Carlo methods, is valuable.
• Experience building knowledge graphs, including entity extraction, relationship extraction, entity resolution, and ontology or schema design, is valuable.
• Bachelor of Science, Master of Science, or equivalent experience.