FDE - Agentic Ai Professional

Data Economy; Hyderabad; Hybrid; INR 350,000 - 600,000; Full time

Job summary

Data Economy in Hyderabad/Pune seeks a Forward Deployed Engineer (FDE) to design, build, deploy, and run AI-powered production systems alongside customers’ engineering teams. You will own outcomes end to end, from prototype to enterprise-scale deployments, and help scale reusable patterns and architectures.

The role blends software engineering rigor with business impact, mentoring peers and driving measurable transformation across customer workflows in a hybrid work environment.

Qualifications

• Production-grade Python with testing and API integration.
• Hands-on AWS Agentic AI with Bedrock.
• Experience building multi-agent systems and tool calling.
• Knowledge of retrieval pipelines and vector search.
• CI/CD, automated testing and monitoring experience.

Responsibilities

• Embed with customer engineering teams to translate business problems into AI-enabled production systems.
• Design and develop production-grade AI applications and orchestrations.
• Own production rollout and monitoring, including CI/CD and resiliency measures.
• Accelerate AI adoption across customer workflows with reusable patterns.
• Mentor engineers and contribute to AI and forward deployment best practices.

Skills

Python

AI/ML production

CI/CD

Production engineering

Multi-agent systems

Education

BTech/MTech/MCA

Tools

Bedrock

LangChain

LangGraph

Strands Agents

Job description

Job Title: FDE - Agentic Ai

Experience Level: 3 - 15 years

Location: Hyderabad / Pune

Qualification: BTech/MTech/MCA

Mode of Work: Hybrid

We are seeking a Forward Deployed Engineer (FDE) works directly inside a strategic enterprise customer to design, build, deploy and run AI-powered production systems alongside the customers 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 customers 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.

Requirements

• 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
  • Agent orchestration with a framework (e.g., Strands Agents, LangGraph, LangChain)
• RAG Retrieval: Building retrieval pipelines, including:
  • Embeddings
  • Chunking
  • Vector search (e.g., Bedrock Knowledge Bases, OpenSearch, pgvector)
• Core AWS Services: Working knowledge of:
  • Serverless and container services (e.g., Lambda, ECS/EKS)
  • API Gateway
  • Storage (e.g., S3, DynamoDB)
  • Security and networking (e.g., IAM)
• Production Engineering: Experience taking AI systems to production, including:
  • CI/CD
  • Automated testing
  • Monitoring

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.