What does a Forward Deployed Engineer do at JDA TSG?

Forward Deployed Engineers at JDA TSG are embedded directly with enterprise customers to implement and customize the platform, build integrations, and solve technical problems in real-world client environments. They combine software engineering skills with direct customer interaction, writing production code while also serving as the primary technical point of contact.

What skills are required for this Azure Machine Learning Customer Engineer role?

The Azure Machine Learning Customer Engineer role at JDA TSG typically requires strong software engineering skills (Python, TypeScript, SQL), experience with APIs and system integration, comfort with cloud platforms, and the ability to communicate complex technical topics to non-technical stakeholders. Most FDE roles require 3-7 years of engineering experience.

Is this Forward Deployed Engineer position at JDA TSG remote?

Remote availability varies by JDA TSG FDE role. Some positions are fully remote, others require travel to customer sites, and some are hybrid. Check the full job description above for the specific location requirements for this posting.

JDA TSG

Azure Machine Learning Customer Engineer

$110K - $1200K Remote Mid Posted May 10, 2026

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Role Description

We’re looking for a forward-thinking Azure Machine Learning Expertto works as a Customer Engineer (CE) who thrives at the intersection of AI innovation, app development, and enterprise-scale delivery. In this strategic role, you’ll help customers leverage the Microsoft Azure AI ecosystem including Azure Machine Learning, Azure OpenAI, AI Foundry, and Copilot Studio to build intelligent apps, custom copilots, and production-ready AI agents. This is a customer-facing role ideal for engineers who want to push the limits of what’s possible with Generative AI, LLMs, and vector-powered applications using tools like CosmosDB.

Facilitate AI strategy and implementation engagements, helping customers adopt and scale Azure Machine Learning, Azure AI Foundry, and Azure OpenAI services
Provide clients guidance to architect and deploy intelligent agents and copilots using Copilot Studio, integrating custom business logic and APIs across enterprise workflows
Guide customers in supporting end-to-end AI development lifecycles—from data ingestion and model training to fine-tuning, deployment, and monitoring
Help clients’ developers and data scientists operationalize models and integrate them into real-time apps using CosmosDB as a scalable backend
Educate customers on responsible AI, model lifecycle management, prompt engineering, vector search, and performance optimization
Conduct workshops, demos, and working sessions to accelerate enterprise AI adoption and foster AI-first thinking across orgs
Surface field feedback to Microsoft product teams and contribute to reusable IP, frameworks, and solution accelerators

Key Skills & Technologies:* AI & ML Platforms: Azure Machine Learning (AutoML, Prompt Flow, Responsible AI), Azure AI Foundry, Azure OpenAI (GPT, Codex, embeddings). * Agent & Copilot Development: Building copilots with Copilot Studio, orchestrating agents via APIs, chaining models & workflows. * App Architecture: AI-infused web apps or chat interfaces powered by CosmosDB, Azure Functions, or App Service. * Data Engineering & Ops: Familiarity with data prep, vector embeddings, model tuning, and inference pipelines. * AI Governance: Prompt safety, cost control, performance tuning, and responsible AI practices. * Developer Experience: Guiding dev teams through prompt engineering, Copilot adoption, and AI integration workflows.

Ideal Candidate Will Have:* 10-15 years overall IT experience including 7 years experience in AI/ML engineering, app development, or cloud architecture roles. * Hands-on experience deploying LLM-based apps, building custom copilots, or fine-tuning OpenAI/GPT models. * Familiarity with Copilot Studio, Prompt Flow, and real-world applications of generative AI and agents. * Strong ability to connect AI innovation to business value, with experience leading technical conversations with devs, data scientists, and execs. * A “builder’s mindset” with a passion for helping customers turn vision into production-ready AI solutions.

About Forward Deployed Engineering

Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.

Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.

Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.