Senior Forward Deployed Engineer – AWS

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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 and digital transformation. The team specializes in building, deploying, and operating AI-driven, cloud-native solutions that modernize mission-critical business operations. By combining advanced engineering, artificial intelligence, data, and cloud technologies, the organization delivers innovative solutions tailored to meet the unique needs of clients across multiple industries.

The organization is seeking a Senior Forward Deployed Engineer – AWS to work closely with enterprise clients in designing, developing, and deploying Generative AI solutions that deliver measurable business value. This role combines hands-on software engineering, solution architecture, and client engagement to rapidly prototype and implement AI-powered applications.

Key Responsibilities

• Partner with enterprise clients to identify business needs and translate high-value Generative AI use cases into scalable solutions.
• Collaborate with business leaders, product owners, architects, and engineering teams to align project priorities and execution.
• Lead workshops and solution discovery sessions to define technical approaches and desired business outcomes.
• Prototype, develop, and deploy AI-powered applications using AWS AI and data services.
• Build AI-enabled platforms, intelligent agents, and enterprise workflows using modern cloud technologies.
• Design scalable AI engineering patterns with appropriate governance, safety, latency, cost optimization, and model risk considerations.
• Develop production-quality software following best practices for testing, CI/CD, logging, documentation, and version control.
• Design extensible application functionality while supporting sprint planning and delivery activities.
• Develop reusable engineering assets including code libraries, prompt libraries, runbooks, and reference implementations.
• Contribute independently within multidisciplinary engineering teams while mentoring junior engineers.

Required Qualifications

• Bachelor’s degree (or equivalent) in Computer Science, Data Science, Engineering, or a related field.
• 5+ 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 with AWS AI & Data services, including one or more of the following:
  • Amazon Bedrock
  • Bedrock AgentCore
  • Strands Agents SDK
  • Knowledge Bases
  • Guardrails
• 1+ year of experience working with AWS Neptune and OpenSearch.
• 1+ year of experience leading project workstreams and translating business requirements into AI-driven 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 writing reliable, maintainable, and well-documented code.
• 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 AWS, Microsoft Azure, or Google Cloud Platform.
• Proven ability to work directly with enterprise client technical teams and senior stakeholders.
• Experience with Spark, Airflow, dbt, streaming technologies, data modeling, feature engineering, experimentation, or machine learning workflows.
• Knowledge of MLOps and LLMOps practices, including evaluation frameworks, model monitoring, and prompt management.
• Experience fine-tuning domain-specific language models using Amazon SageMaker.
• Experience integrating LLM applications with enterprise systems through APIs, microservices, or event-driven architectures.
• Experience working within hybrid onshore and offshore engineering teams.
• Familiarity with enterprise security, privacy, governance, and compliance requirements.

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