Gen AI Forward Deployed Engineer/ Architect || Bangalore/Chennai/Noida/Hyderabad

HCLTech; Bengaluru; Hybrid; INR 1,800,000 - 2,800,000; Full time

Job summary

HCLTech is seeking a Gen AI FDE Engineer to lead the deployment of Generative AI and agentic solutions across customer environments, translating designs into production-ready implementations with reliability and scalability.

You will own deployments from pilot to production, extend AI components, build RAG pipelines and agent workflows, and collaborate with cross-functional teams to ensure quality, safety, and governance.

Qualifications

• Proficient Python and software development fundamentals.
• Experience with APIs, microservices, and system integration.
• Hands-on with cloud platforms such as Azure, AWS, or GCP.
• Experience with Generative AI/LLM deployments and agent frameworks.
• Strong problem-solving and debugging across distributed systems.
• Good communication and cross-functional collaboration.

Responsibilities

• Own customer deployments across pilot and production phases.
• Extend AI solution components including APIs and workflows.
• Build and configure RAG pipelines and agent workflows.
• Test, validate, and evaluate GenAI solutions for performance.
• Monitor system performance and diagnose issues across components.
• Collaborate across data, APIs, and platform layers.
• Follow best practices for safety and governance in AI.
• Produce high-quality documentation and reusable components.
• Collaborate with cross-functional teams to plan solution delivery.
• Lead debugging and troubleshooting in prod and non-prod.
• Mentor junior engineers.

Skills

Python

APIs & microservices

Cloud platforms

Generative AI

OpenAI / Azure OpenAI

Agent frameworks

Data integration

Debugging

Communication

Job description

HCLTech is hiring Gen AI FDE Engineer || Noida/Bangalore/Chennai/Hyderabad

Job Summary

Lead the deployment of Generative AI and agentic solutions in customer environments by independently executing and owning implementation tasks across integration, testing, and stabilization phases. Collaborate with senior engineers, architects, and cross-functional teams to translate solution designs into production-ready implementations, ensuring high standards of reliability, scalability, and quality.

• Execute and own customer deployments across pilot and production phases, ensuring successful delivery outcomes
• Implement and extend AI solution components, including APIs, workflows, integrations, and orchestration layers
• Build and configure RAG pipelines, agent workflows, and orchestration layers with increasing independence
• Perform testing, validation, and evaluation of GenAI solutions, ensuring alignment with performance and quality benchmarks
• Monitor system performance, diagnose issues, and drive resolution across components
• Work across data, APIs, and platform layers to ensure robust and scalable system integration
• Follow and reinforce best practices for safety, governance, and responsible AI implementation
• Produce high-quality documentation, reusable components, and implementation patterns
• Collaborate closely with cross-functional teams (engineering, platform, support) and contribute to solution delivery planning
• Lead debugging and troubleshooting efforts in both production and non-production environments
• Mentor junior engineers (FDE I / II) and support knowledge sharing within the team

Key Skills (Mandatory)

• Strong proficiency in Python and software development fundamentals
• Solid understanding of APIs, microservices, and system integration patterns
• Hands-on experience with cloud platforms (Azure / AWS / GCP)
• Practical experience with Generative AI / LLM applications and deployment patterns
• Strong problem-solving and debugging skills across distributed systems
• Good communication skills, including ability to collaborate with cross-functional teams
• Experience with OpenAI / Azure OpenAI or similar APIs
• Working knowledge of RAG, prompt engineering, and evaluation frameworks
• Hands-on familiarity with agent frameworks (LangChain, LangGraph, etc.)
• Knowledge of databases, vector stores, and data pipelines
• Exposure to monitoring, logging, observability, and performance tuning tools