Lead Forward Deployed Engineer, Microsoft AI & Data

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Company Overview

A leading global professional services organization is expanding its AI & Engineering practice to help enterprise clients accelerate AI adoption at scale. The team specializes in designing, deploying, and operating AI-powered, data-driven, and cloud-based solutions that modernize mission-critical business operations. Through a combination of engineering excellence, industry expertise, and collaborative delivery models, the organization enables clients to transform their technology platforms and maximize business value using Generative AI and advanced cloud technologies.

Key Responsibilities

• Lead forward-deployed engineering teams responsible for building and deploying production-ready Generative AI solutions.
• Establish technical direction while remaining actively involved in system architecture, code reviews, debugging, and solution development.
• Build trusted relationships with executive stakeholders and serve as the primary engineering advisor for strategic client engagements.
• Lead discovery sessions, define project success metrics, and develop scalable implementation roadmaps from prototype through production.
• Align executive sponsors, IT leadership, and business stakeholders around AI transformation strategies.
• Represent the engineering practice during client engagements, executive briefings, and platform partnership initiatives.
• Lead engineering pods consisting of onshore and offshore team members while managing execution, delivery quality, resources, and escalations.
• Maintain delivery excellence through sprint planning, stakeholder communication, risk management, and quality assurance processes
• Mentor and develop engineering team members through technical guidance and leadership.
• Architect and oversee the delivery of AI-powered applications, including copilots, intelligent assistants, agentic workflows, and enterprise knowledge search solutions.
• Guide prompt engineering strategies, AI tool integration, and human-in-the-loop workflows.
• Design and govern Retrieval-Augmented Generation (RAG) pipelines, including data ingestion, chunking, embeddings, vector databases, hybrid search, and production scalability.
• Define evaluation frameworks to measure AI quality, safety, latency, hallucination risk, governance, and operational performance.
• Guide the architecture of scalable data pipelines supporting AI applications.
• Implement engineering best practices across testing, CI/CD, logging, monitoring, documentation, version control, and data governance.
• Design and support cloud-native AI solutions across Azure and other major cloud platforms.

Required Qualifications

• Bachelor’s degree (or equivalent) in Computer Science, Data Science, Engineering, or a related discipline.
• 7+ years of experience in software engineering, data engineering, data science, or analytics engineering.
• 1+ year of hands-on experience building and deploying Generative AI or Large Language Model (LLM) solutions in production or client environments.
• 1+ year of experience working with Microsoft AI & Data technologies, including Azure AI Foundry.
• 1+ year of experience leading technical project workstreams and translating business requirements into AI-driven solutions.
• 1+ year of experience developing reliable, maintainable, and well-documented software.
• Strong client-facing communication and stakeholder management skills.
• Willingness to travel approximately 50% based on client and project requirements.
• Eligibility to work in accordance with applicable employment and immigration requirements.

Preferred Qualifications

• Experience with cloud platforms including Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP).
• Experience collaborating directly with client engineering teams in fast-paced delivery environments.
• Knowledge of data engineering technologies such as Apache Spark, Airflow, dbt, streaming platforms, and data modeling.
• Experience with machine learning, feature engineering, experimentation, or model evaluation.
• Familiarity with MLOps and LLMOps practices, including model monitoring, prompt management, and evaluation frameworks.
• Experience integrating LLM applications with enterprise APIs, microservices, and event-driven architectures.
• Experience leading hybrid onshore and offshore engineering teams.
• Understanding of enterprise security, privacy, governance, and regulatory compliance requirements.

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