Forward Deployed Engineer – AWS

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Company Overview

A leading global professional services organization is seeking a Forward Deployed Engineer – AWS to help enterprise clients accelerate AI adoption through innovative, production-ready solutions. The organization specializes in delivering AI, cloud, data, and engineering services that enable businesses to modernize operations, improve decision-making, and achieve measurable business outcomes. Working alongside cross-functional teams, engineers play a critical role in designing and deploying scalable AI-powered applications across diverse industries.

The organization is seeking a Forward Deployed Engineer – AWS to collaborate directly with enterprise clients in designing, prototyping, and delivering high-impact Generative AI solutions. This role combines technical engineering expertise with client engagement, requiring professionals who can rapidly build production-ready AI applications while partnering with stakeholders to solve complex business challenges.

Key Responsibilities

• Partner with enterprise clients to identify business needs and translate high-value Generative AI use cases into practical solutions.
• Collaborate with product owners, architects, engineers, and business leaders to align priorities and deliver successful outcomes.
• Lead client workshops and technical working sessions to define solution strategies.
• Prototype, develop, and deploy AI-enabled applications using AWS AI technologies.
• Contribute independently within an engineering delivery team while mentoring junior engineers.
• Build AI-powered applications, agentic workflows, and enterprise AI platforms.
• Develop scalable AI engineering patterns, tool integrations, and human-in-the-loop workflows.
• Make architecture decisions that balance performance, quality, security, latency, cost, and model governance.
• Deliver production-quality software using best practices in testing, CI/CD, logging, version control, and documentation.
• Design extensible application components and support sprint planning and solution estimation.
• Develop reusable assets including code libraries, prompt templates, runbooks, and reference implementations.
• Collaborate across engineering teams to modernize technology platforms and deliver business value.

Required Qualifications

• Bachelor’s degree (or equivalent) in Computer Science, Data Science, Engineering, or a related field.
• 3+ 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 AWS AI & Data technologies, including one or more of the following:
  • Amazon Bedrock
  • Bedrock AgentCore
  • Strands Agents SDK
  • Knowledge Bases
  • Guardrails
• 1+ year of experience with AWS Neptune and OpenSearch.
• 1+ year of experience leading project workstreams and translating business requirements into AI solutions.
• Possession of at least three of the following AWS certifications:
  • AWS Certified Cloud Practitioner
  • AWS Certified Solutions Architect – Associate
  • AWS Certified AI Practitioner (AIF-C01)
  • AWS Certified Generative AI Developer – Professional (AIP-C01)
  • AWS Certified Machine Learning Engineer – Associate (MLA-C01)
  • AWS Certified Data Engineer – Associate
• Experience building reliable, maintainable, well-documented software applications.
• Willingness to travel up to 50%, depending on client and project requirements.
• Eligibility to meet work authorization requirements where applicable.

Preferred Qualifications

• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Experience working directly with enterprise client technical teams in fast-paced delivery environments.
• Background in data engineering technologies including Spark, Airflow, dbt, streaming platforms, or data modeling.
• Experience with machine learning engineering, feature engineering, experimentation, or model evaluation.
• Knowledge of MLOps or LLMOps practices, including evaluation frameworks, prompt management, and model monitoring.
• Experience with Amazon SageMaker fine-tuning techniques for domain-specific language models.
• Experience integrating LLM solutions using APIs, microservices, or event-driven architectures.
• Experience working within hybrid onshore/offshore engineering teams.
• Familiarity with enterprise security, privacy, governance, and compliance best practices.

Disclaimer

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