AI Forward Deployed Engineer

About the Role

The AI Forward Deployed Engineer partners directly with business stakeholders to identify high-value opportunities and convert them into secure, scalable, and measurable AI solutions. This role combines strong software engineering, generative AI, and machine learning capabilities with stakeholder-facing delivery. It owns the journey from discovery and rapid prototyping through enterprise integration, production deployment, adoption, and continuous improvement.

Key Responsibilities

• Assess priority business opportunities, existing AI capabilities, data sources, platforms, integrations, and delivery constraints.
• Define business objectives, user needs, success measures, adoption barriers, and technical requirements with key stakeholders.
• Create and maintain a roadmap based on business value, feasibility, data readiness, risk, and resource needs.
• Develop rapid prototypes to validate technical feasibility, user value, and solution direction.
• Build, deploy, and operationalize selected AI solutions with appropriate security, scalability, reliability, and support readiness.
• Integrate AI capabilities with enterprise applications, identity systems, APIs, data sources, and operational workflows.
• Establish evaluation, error-analysis, logging, metrics, tracing, usage, and cost-monitoring mechanisms.
• Deliver architecture documentation, runbooks, reusable patterns, training, and knowledge transfer for users and support teams.

Requirements & Tech Stack

• Software Engineering:
• Strong Python programming.
• Backend and API development using FastAPI, Flask REST, or equivalent.
• REST APIs, SQL, relational databases, Git, testing, debugging, and code review.
• Generative AI and Context Engineering:
• Large language models, prompt and context engineering, RAG and Graph RAG, embeddings, semantic search, vector databases, tool/function calling, memory, agent orchestration, and evaluation.
• Machine Learning:
• ML and deep-learning fundamentals, natural language processing, model evaluation, data preprocessing, and experience with PyTorch and/or TensorFlow.
• Production Engineering:
• Containerization, CI/CD, cloud or on-premises deployment, observability, performance tuning, scalability, resilience, and production troubleshooting.
• Familiarity with multi-model architectures, AI gateways, model routing, token and cost management, and model selection is preferred.
• Data and Integration:
• Data pipelines, enterprise application integration, identity and access management, secure configuration, and interoperability across systems.
• AI Quality and Governance:
• Evaluation frameworks, error analysis, guardrails, responsible AI, data privacy, security, access control, lifecycle management, and usage/cost monitoring.
• Forward Deployed Delivery:
• Stakeholder discovery, problem decomposition, rapid prototyping, ambiguity management, end-to-end ownership, documentation, and knowledge transfer.
• Good to Have:
• Microsoft Azure and Azure OpenAI.
• LangGraph, LangChain, or comparable frameworks.
• Qdrant, Milvus, pgvector, or FAISS.
• Docker, Kubernetes, PostgreSQL, and Redis.
• OpenTelemetry, Prometheus, and Grafana.
• LiteLLM or comparable AI gateways.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical discipline, or equivalent practical experience.
• Hands-on experience developing software, machine learning, or generative AI solutions through internships, academic projects, or professional environments.
• Production deployment experience is preferred.
• Demonstrated ability to translate business requirements into practical technical solutions.
• Effective communication with technical and non-technical stakeholders.

Key Behaviors

• Customer and Business Focus: Builds trusted stakeholder relationships and anchors solutions to measurable user and business outcomes.
• Ownership: Takes responsibility from discovery through production stabilization, adoption, and continuous improvement.
• Problem Solving: Uses structured analysis and hands-on troubleshooting across application, model, data, integration, and infrastructure layers.
• Agility and Adaptability: Works effectively with ambiguity, changing priorities, and evolving AI technologies.
• Collaboration and Communication: Explains complex topics clearly and works effectively across Product, Engineering, Architecture, Infrastructure, Security, and business teams.
• Continuous Learning: Actively evaluates new approaches while applying sound engineering judgment, governance, and evidence-based decision-making.
• Attention to Detail: Maintains high standards for quality, security, documentation, and operational readiness.