Forward Deployed Engineer(FDE)

No. of Openings: 8(Interviews conducted daily)

NOTE: Immediate joiners highly preferred.

Mandatory Skills:

• AWS Bedrock along with Agentcore
• Python
• Agentic AI
• Fast API

Role Overview

A Forward Deployed Engineer (FDE) works directly inside a strategic enterprise customer to design, build, deploy and run AI-powered production systems alongside the customer's engineering team. FDEs write production-grade software and own outcomes end to end, from prototype through enterprise-scale deployment.

The role combines the technical rigor of a software engineer with the urgency and ownership needed to deliver measurable business outcomes. FDEs also create reusable architectures, patterns and engineering practices that help the wider FDE practice scale.

Key Responsibilities:

• Embed with customer engineering teams: Understand the customer's business processes, technical architecture and operational constraints. Turn ambiguous business problems into scalable, AI-enabled production systems.
• Build production AI applications: Design and develop production-grade software for AI and agentic workflows, including multi-agent orchestration, retrieval pipelines, workflow automation and decision intelligence. Integrate foundation models, customer data sources, APIs and existing applications into cohesive AI experiences. Optimize for latency, reliability, observability, cost and security.
• Own production: Take systems from design through production rollout and ongoing operations. Troubleshoot incidents across AI models, distributed systems, data pipelines and application services. Put in place monitoring, evaluations, guardrails, testing, CI/CD, rollback and resiliency mechanisms that keep AI systems healthy at scale.
• Accelerate customer transformation: Identify opportunities to expand AI adoption across customer workflows. Build reusable patterns, accelerators and reference architectures for future engagements.
• Raise the bar: Mentor engineers and contribute to engineering best practices for AI and forward deployment. Feed reusable components and learnings back into products and services.

Core technical skills (must have):

Python: Strong, production-grade Python: clean code, testing, APIs and integration with enterprise systems.

Agentic AI on AWS: Hands-on experience building multi-agent systems on Amazon Bedrock and Bedrock AgentCore, including prompt engineering, tool calling and agent orchestration with a framework (e.g. Strands Agents, LangGraph, LangChain).

RAG & retrieval: Building retrieval pipelines: embeddings, chunking and vector search (e.g. Bedrock Knowledge Bases, OpenSearch, pgvector).

Core AWS services: Working knowledge of serverless and container services, storage, security and networking on AWS (e.g. Lambda, API Gateway, ECS/EKS, S3, DynamoDB, IAM).

Production engineering: Taking AI systems to production: CI/CD, automated testing, monitoring, evaluations and guardrails for AI output.

Good to have:

• Infrastructure as code (e.g. Terraform, AWS CDK) and containers (Docker).
• LLM evaluation and tracing tools (e.g. Bedrock Guardrails, RAGAS, Langfuse, OpenTelemetry).
• MCP (Model Context Protocol), agent-to-agent (A2A) patterns, or other frameworks such as CrewAI or LlamaIndex.
• API frameworks such as FastAPI, SQL, and event-driven services (e.g. Step Functions, SQS, EventBridge).
• A second language such as TypeScript/Node.js or Java.
• Amazon SageMaker, and model fine-tuning or customization on Bedrock.
• Experience in regulated industries (banking, capital markets, insurance): security reviews, audit and data-residency controls.
• AWS certifications: AI Practitioner, Machine Learning Engineer – Associate, Solutions Architect – Associate/Professional, or Generative AI Developer – Professional.

Basic qualifications:

• 5+ years of professional software development experience (excluding internships).
• 5+ years of programming in at least one software programming language.
• 5+ years leading design or architecture of new and existing systems (design patterns, reliability and scaling).
• Experience as a mentor, tech lead or leading an engineering team.

Preferred qualifications:

• Experience building production AI or ML applications, including agentic workflows.
• 5+ years across the full software development life cycle: coding standards, code reviews, source control, build processes, testing and operations.
• Bachelor’s degree in computer science or equivalent.
• Strong written and verbal communication skills; comfortable working in customer-facing environments.