Forward Deployed Engineer

Hallmark Global Solutions Ltd; New York (NY); On-site; USD 150,000 - 230,000; Full time

Job summary

Hallmark Global Solutions Ltd is seeking a hands-on Forward Deployed Engineer to bridge business intent and production-grade AI solutions. You will work directly with stakeholders, translate ideas into deployable AI systems, and lead delivery with enterprise governance.

This role covers solution engineering, AI engineering, and delivery leadership from problem discovery through deployment and optimization, ensuring measurable business outcomes.

Qualifications

• Strong background in software engineering and backend systems.
• Proficiency in at least one modern language (Python/Java/Go).
• Solid understanding of system design, scalability, and reliability.
• Experience with hyperscale cloud platforms (AWS, GCP, Azure).

Responsibilities

• Partner with stakeholders to understand workflows and outcomes.
• Convert business intent into AI-enabled solution designs and architectures.
• Deploy complex enterprise integrations and ensure security and reliability.
• Drive rapid iterations while maintaining enterprise-grade quality.

Skills

Software engineering

Python/Java/Go

System design

Cloud platforms

Agentic AI

LLMs in enterprise

Prompt engineering

DevSecOps

Tools

Cursor

GitHub Copilot

Semantic Kernel

LangGraph

Job description

The Forward Deployed Engineer (FDE) is a hands-on, customer-facing engineering leader who bridges business intent and production-grade AI solutions. The FDE works directly with business stakeholders, product owners, and platform teams to translate ideas into deployable solutions using an enterprise-enabled agentic AI platform and a governed adoption framework.

This role blends solution engineering, AI engineering, and delivery leadership, with strong ownership from problem discovery → architecture → build → deployment → optimization. The FDE operates close to customers and internal product teams, ensuring solutions deliver measurable business outcomes while meeting enterprise, security, and compliance standards.

Key Responsibilities

• Partner with business stakeholders to understand problem statements, workflows, and desired outcomes


• Convert business intent into AI-enabled solution designs, agent workflows, and system architectures


• Deploying enterprise complex systems integrations while solving critical business problems


• Drive rapid iteration while maintaining enterprise-grade quality, security, and reliability

2. Agentic AI Solution Engineering

• Design and implement agentic workflows using enterprise agentic AI platforms


• Orchestrate multi-agent systems that handle reasoning, planning, execution, validation, and monitoring


• Encode business logic, SOPs, policies, and controls into autonomous or semi-autonomous agents


• Apply human-in-the-loop, guardrails, and fallback mechanisms where required


• Work with frontier foundation models (LLMs, multimodal models) and enterprise-approved model stacks


• Prompt design and prompt chaining


• Tool grounding and retrieval-augmented generation (RAG)


• Knowledge graph and memory integration


• Optimize solutions for accuracy, latency, cost, and reliability

4. AI-First Engineering & Developer Tooling

• Leverage AI-assisted development tools to accelerate delivery:


• Cursor


• AI-assisted testing, code review, and refactoring tools


• Establish AI-augmented engineering workflows across design, build, test, and release phases


• Coach teams on effective human-AI collaboration in engineering


• Design and implement intelligent CI/CD pipelines integrating:


• AI-generated code and test artifacts


• Policy and control validation


• Automated security and compliance checks


• Integrate agentic workflows into DevSecOps / MLOps pipelines


• Ensure repeatable, auditable, and scalable deployments across environments

6. Enterprise Readiness & Governance Alignment

• Ensure solutions comply with:


• Security, privacy, and data-handling policies


• Model risk management and AI governance frameworks


• Regulatory and audit requirements (especially in regulated industries)


• Collaborate with platform, security, and governance teams to operationalize guardrails


• Contribute patterns, blueprints, and reusable assets to the enterprise AI platform


• Act as a trusted technical advisor to customers and internal stakeholders


• Present architectures, demos, and outcomes to engineering leaders, business heads, and executives


• Gather feedback from production usage and continuously improve solutions


• Serve as the "voice of the customer" back into platform and product teams

Required Skills & Experience

Core Engineering & Architecture

• Strong background in software engineering (backend, APIs, distributed systems)


• Proficiency in at least one modern programming language (Python, Java, Go, or similar)


• Solid understanding of system design, scalability, and reliability


• Skilled in hyperscale platforms (AWS, GCP, Azure, OpenShift)

Agentic AI & AI Engineering

• Hands-on experience with agentic AI frameworks and orchestration patterns (n8n, LangGraph, Semantic Kernel, CrewAI, etc.)


• Experience working with LLMs / foundation models in enterprise settings (Claude, Gemini, OpenAI)


• Strong skills in prompt engineering, context engineering, and tool integration


• Understanding of RAG, memory systems, and knowledge grounding


• Spec driven development – Architecture, Security and Application frameworks.

AI Tooling & Productivity

• Practical experience using Cursor, GitHub Copilot, or similar AI coding tools


• Familiarity with AI-assisted testing, documentation, and code review


• Ability to design AI-first developer workflows

DevOps, CI/CD & Platform Integration

• Experience with CI/CD pipelines, infrastructure as code, and cloud platforms


• Understanding of DevSecOps and automated control enforcement


• Familiarity with MLOps concepts for model lifecycle and monitoring


• Strong problem-solving and analytical mindset


• Ability to work in ambiguous, fast-moving environments


• Excellent communication skills with both technical and non-technical stakeholders


• Customer-centric mindset with ownership and accountability


• Good handle on Complex enterprise system integrations

Preferred Qualifications

• Experience in regulated industries (banking, financial services, healthcare, etc.)


• Exposure to AI governance, model risk, and compliance frameworks


• Prior experience in customer-facing engineering roles (FDE, Solutions Engineer, Field Engineer)


• Experience contributing to platform blueprints, accelerators, or internal frameworks

What Success Looks Like

• Business ideas move to production faster and with higher confidence


• Agentic AI solutions deliver measurable business outcomes


• Engineering teams adopt AI-first workflows with strong governance


• Customers trust the platform and the FDE as a strategic delivery partner