Middle/Senior Forward Deployed Engineer (Python, AI)

exadelinc; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid or on-site client delivery

Top-tier equipment provision

Learning and reimbursement programs

Job summary

exadelinc is seeking a forward-deployed engineer blending hands-on AI software development with client-facing delivery inside enterprise environments. You will embed onsite with operational teams and executive sponsors, translating business problems into prioritized opportunities and shipping production-grade AI agents and data pipelines.

Senior candidates will lead pod execution, own engagement outcomes, and mentor other forward-deployed engineers, contributing to reusable reference

Qualifications

• Mid-level hands-on technical experience with client-facing capacity; senior track requires delivery leadership across multidisciplinary teams or pods.
• Proven record of delivering production software used by real users and business teams, including LLM-backed or agentic systems.
• Advanced proficiency in Python, plus TypeScript or JavaScript and SQL.
• Strong knowledge of prompt and context engineering, agent architecture, tool and function calling, retrieval-augmented generation, and frameworks such as the OpenAI Agents SDK or Claude SDKs.
• Experience with evaluation harnesses, drift monitoring, observability, human-in-the-loop control design, data modeling, ETL/ELT pipelines, vector stores, and embedding pipelines.
• Cloud deployment experience on AWS, Azure, or GCP, plus familiarity with Docker, Kubernetes, CI/CD, infrastructure as code, REST or GraphQL APIs, and SSO.
• Willingness to travel approximately 25-50% and work onsite in client environments, with fluent English communication.

Responsibilities

• Lead discovery sessions with client stakeholders to map business systems, data, operating models, and underlying challenges.
• Translate ambiguous business problems into quantified opportunity maps and deliver working, data-grounded prototypes early in each engagement.
• Design, develop, debug, and deploy production-grade AI agents, integrations, data pipelines, and applications directly within client systems.
• Define evaluation frameworks, quality thresholds, regression gates, monitoring strategies, and human-in-the-loop controls for AI and agentic workflows.
• Scope, sequence, and coordinate delivery across engineers, architects, and specialist contributors, making explicit trade-offs between scope, speed, quality, and value.
• Convert successful deployments into reusable reference architectures, agent templates, evaluation frameworks, and delivery playbooks, while training client teams to operate and extend the work.
• Nice to have: Java, C#, or C++ within legacy enterprise environments.
• Nice to have: Node.js, React, or Next.js for client-facing applications.
• Nice to have: Fine-tuning fundamentals and multi-agent orchestration experience.
• Nice to have: TDD or BDD practices, large-scale data platforms such as Spark, Databricks, or Snowflake, and on-premises or sovereign deployment familiarity.
• Nice to have: Experience with Jira, Azure DevOps, ServiceNow, Salesforce, or SAP, and GitHub or GitLab-based delivery workflows.

Skills

Python

TypeScript

JavaScript

SQL

Prompt engineering

Agent architecture

LLM integration

Delivery leadership

Client-facing

English communication

Travel readiness

Tools

Docker

Kubernetes

CI/CD

REST

GraphQL

SSO

AWS

Azure

GCP

Jira

Azure DevOps

ServiceNow

Salesforce

SAP

GitHub

GitLab

Job description

Role overview

A forward-deployed engineering role pairing hands-on AI software development with client-facing delivery inside enterprise environments. Engineers embed onsite with operational teams and executive sponsors, translate ambiguous business problems into prioritized opportunities, and ship production-grade AI agents, data pipelines, and applications. Senior-level candidates additionally lead pod execution, own engagement outcomes, and mentor other forward-deployed engineers.

Responsibilities

• Lead discovery sessions with client stakeholders to map business systems, data, operating models, and underlying challenges.
• Translate ambiguous business problems into quantified opportunity maps and deliver working, data-grounded prototypes early in each engagement.
• Design, develop, debug, and deploy production-grade AI agents, integrations, data pipelines, and applications directly within client systems.
• Define evaluation frameworks, quality thresholds, regression gates, monitoring strategies, and human-in-the-loop controls for AI and agentic workflows.
• Scope, sequence, and coordinate delivery across engineers, architects, and specialist contributors, making explicit trade-offs between scope, speed, quality, and value.
• Convert successful deployments into reusable reference architectures, agent templates, evaluation frameworks, and delivery playbooks, while training client teams to operate and extend the work.

Requirements

• Mid-level hands-on technical experience with client-facing capacity; senior track requires delivery leadership across multidisciplinary teams or pods.
• Proven record of delivering production software used by real users and business teams, including LLM-backed or agentic systems.
• Advanced proficiency in Python, plus TypeScript or JavaScript and SQL.
• Strong knowledge of prompt and context engineering, agent architecture, tool and function calling, retrieval-augmented generation, and frameworks such as the OpenAI Agents SDK or Claude SDKs.
• Experience with evaluation harnesses, drift monitoring, observability, human-in-the-loop control design, data modeling, ETL/ELT pipelines, vector stores, and embedding pipelines.
• Cloud deployment experience on AWS, Azure, or GCP, plus familiarity with Docker, Kubernetes, CI/CD, infrastructure as code, REST or GraphQL APIs, and SSO.
• Willingness to travel approximately 25-50% and work onsite in client environments, with fluent English communication.

Nice to have

• Java, C#, or C++ within legacy enterprise environments.
• Node.js, React, or Next.js for client-facing applications.
• Fine-tuning fundamentals and multi-agent orchestration experience.
• TDD or BDD practices, large-scale data platforms such as Spark, Databricks, or Snowflake, and on-premises or sovereign deployment familiarity.
• Experience with enterprise systems such as Jira, Azure DevOps, ServiceNow, Salesforce, or SAP, and GitHub or GitLab-based delivery workflows.

Benefits and work setup

• International project exposure with in-office, hybrid, or remote flexibility depending on location and contract type.
• Medical healthcare, recognition program, ongoing learning and reimbursement, well-being program, team events, sports compensation, referral bonuses, and top-tier equipment provision.