Forward Deployed Software Engineer Hcltech Noida Hyderabad Bengaluru

Vibehackers; Hyderabad; On-site; INR 3,600,000 - 6,000,000; Full time

Job summary

Vibehackers is seeking an experienced engineer to lead end-to-end deployments of Generative AI and agentic systems, owning implementation across pilot, stabilization, and production phases. You will work with architects, product and platform teams to deliver scalable, reliable solutions that meet governance and performance expectations.

Responsibilities include designing architectures, building RAG pipelines, and guiding junior engineers, while ensuring safety and observability throughout

Qualifications

• Strong Python and software engineering practices.
• Experience with APIs, microservices, and distributed systems.
• Production experience with Generative AI/LLM deployments.
• Cloud deployment experience on Azure/AWS/GCP.
• Excellent problem-solving and debugging skills.

Responsibilities

• Lead end-to-end customer deployments across pilot, stabilization, and production.
• Design AI solution architectures including APIs, workflows, and orchestrations.
• Build and optimize RAG pipelines, agent workflows, and orchestration layers.
• Define testing, validation, and evaluation frameworks.
• Monitor performance and diagnose/root-cause issues across layers.
• Ensure scalable, resilient systems with data/API/platform integration.
• Drive safety, governance, and responsible AI practices.
• Create reusable patterns, accelerators, and docs to improve delivery.
• Mentor junior engineers and participate in customer discussions.

Skills

Python

APIs

Microservices

Distributed systems

LLM APIs

LangChain

Debugging

Observability

Performance optimization

Cloud platforms

LangGraph

AutoGen

Tools

Azure

AWS

GCP

LangChain

LangGraph

AutoGen

Job description

Focused on building and deploying LLM-based systems and agent frameworks (RAG, LangChain, etc.), so it's directly about shipping AI-driven dev solutions.

About the Role

Lead end-to-end deployment of Generative AI and agentic systems for customers, owning integration, testing, stabilization, and production rollout. Collaborate with architects, product, and platform teams to design scalable, reliable AI solutions and drive best practices for governance, performance, and operational reliability.

Job Description

Role

Lead customer deployments of production-grade Generative AI and agentic systems, owning end-to-end implementation across pilot, stabilization, and production phases. Work with architects, product, and platform teams to design and deliver scalable, reliable solutions that meet governance and performance expectations.

Key Responsibilities

• Lead end-to-end customer deployments across pilot, stabilization, and production.
• Design and implement AI solution architectures, including APIs, workflows, integrations, and orchestration layers.
• Build and optimize RAG pipelines, agent workflows, and orchestration frameworks for real-world use cases.
• Define and execute testing, validation, and evaluation frameworks to ensure solution quality and performance.
• Monitor system performance; identify, diagnose, and resolve issues across system layers.
• Integrate across data, APIs, and platform components to ensure scalable and resilient systems.
• Drive safety, governance, and responsible AI practices across deployments.
• Create reusable solution patterns, accelerators, and documentation to improve delivery efficiency.
• Lead debugging and troubleshooting in production; mentor and guide junior FDE engineers.
• Participate in customer discussions, offering technical inputs, trade-offs, and recommendations.

Requirements

Mandatory

• Strong expertise in Python and software engineering best practices.
• Deep understanding of APIs, microservices, and distributed system integration.
• Hands-on experience with cloud platforms (Azure, AWS, GCP) and deployment architectures.
• Proven experience working with Generative AI / LLM-based systems in production.
• Strong problem-solving and system-level debugging skills.
• Effective communication skills for engagement with customers and internal stakeholders.
• Experience with OpenAI / Azure OpenAI or similar LLM APIs.
• Strong knowledge of RAG architectures, prompt engineering, and evaluation frameworks.
• Hands-on experience with agent frameworks (LangChain, LangGraph, AutoGen, etc.).
• Familiarity with vector databases, data pipelines, and large-scale data handling.
• Experience with observability, monitoring, and performance optimization tools.
• Exposure to cost, performance, and reliability optimization for AI systems.

Skills

Software Engineering System Integration System Design Distributed Systems APIs Microservices Testing and Validation Monitoring and Observability Debugging Performance Optimization Responsible AI / Governance Collaboration Customer Engagement Mentoring