This is a senior, hands-on engineering role for someone who owns the full lifecycle of AI systems - from data pipeline design and model development to production deployment and continuous improvement. You’ll work closely with firm stakeholders and leadership to shape and deliver impactful AI-driven solutions.
Key Responsibilities
Partner with the Director of AI to evaluate, implement, and integrate AI tools aligned with business and departmental needs.
Lead the end-to-end design and delivery of AI systems, including RAG pipelines, fine-tuned LLMs, and agent-based workflows to automate complex processes.
Own MLOps practices: CI/CD pipelines, model evaluation, monitoring, A/B testing, and rollback strategies.
Translate business and operational requirements into technical roadmaps, prototypes, and scalable solutions in collaboration with cross-functional teams.
Design secure data ingestion pipelines for sensitive datasets, ensuring compliance and data protection.
Implement vector databases and retrieval strategies to support high-quality, context-aware AI applications.
Establish engineering best practices, including coding standards, testing frameworks, and observability; mentor and support junior engineers.
Evaluate emerging GenAI tools, open-source models, and vendor platforms; provide recommendations on build vs. buy decisions.
Develop integrations, scripts, and automation to connect AI capabilities with internal systems (e.g., document management, CRM, enterprise platforms).
Build prototypes and proof-of-concepts to validate AI use cases in real-world environments.
Design and maintain APIs and services to enable interoperability across platforms.
Conduct technical assessments of AI vendors, focusing on scalability, security, and integration feasibility.
Support pilot programs and early-stage deployments, ensuring solutions are production-ready.
Stay current with advancements in AI/ML technologies relevant to enterprise applications.
Document system architectures, integration patterns, and operational processes.
Act as a technical partner to leadership, turning strategic AI initiatives into practical, deliverable solutions.
Requirements
Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
8+ years of software engineering experience, including 3+ years building and deploying ML/AI systems in production.
Expert-level Python and strong experience with deep learning frameworks (PyTorch or TensorFlow/Keras).
Hands-on experience with modern GenAI tooling (e.g., Hugging Face, LangChain or LlamaIndex, RAG systems, fine-tuning, RLHF).
Strong cloud experience (AWS or GCP), including container orchestration (EKS/ECS, GKE), infrastructure-as-code (Terraform/CloudFormation), and GPU workloads.
Experience designing APIs and distributed systems, including REST, GraphQL, and gRPC.
Proven ability to work with stakeholders to define problems, prioritize solutions, and balance business value with technical performance.
Experience integrating with APIs, SDKs, and SaaS platforms.
Strong problem-solving skills with the ability to independently research and implement solutions.
Excellent communication skills, with the ability to collaborate across technical and non-technical teams.