SME - Terraform,Python,Google Cloud Build,Ansible
United Kingdom
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Job Description
SME - Terraform,Python,Google Cloud Build,Ansible
Greater London, England

Job Summary

Job Summary : AI Engineer Engineer is expected to perform the following Services and/or provide the following deliverables: Agentic AI Engineer: • Lead design and implementation of multi-agent and RAG pipeline systems • Integrate LLMs and manage prompt optimization using LangChain, LangGraph, and Google ADK • Develop secure APIs (FastAPI/Flask) with observability and performance tuning • Implement and maintain MCP servers for orchestrating multi-agent workflows • Conduct A/B testing for LLM behavior, accuracy, and cost efficiency • Develop RESTful APIs (FastAPI/Flask) for LLM and agentic workflows • Implement authentication (OAuth2), logging, and observability • Optimize backend throughput for multi-agent systems • Provision and manage GCP infrastructure for the AI environment, MCP servers, and Streamlit UI • Implement infrastructure security aligned with DB CSO/CISO requirements and HCLTech patterns • Set up monitoring, alerting, and observability stacks (Prometheus, Grafana, Cloud Monitoring) • Manage service accounts, IAM policies, and network security for sandbox and production environments • Languages/Frameworks: Python, FastAPI, Flask • GenAI Frameworks: LangChain, LangGraph, Google ADK, Hugging Face • Databases: PostgreSQL, MongoDB, Redis, FAISS • Cloud: GCP (Vertex AI), Kubernetes • Agent Systems: MCP server, A2A communication

Job Description : AI Engineer\\\\r\\\\n\\\\r\\\\nEngineer is expected to perform the following Services and/or provide the following deliverables: \\\\r\\\\nAgentic AI Engineer:\\\\r\\\\n• Lead design and implementation of multi-agent and RAG pipeline systems\\\\r\\\\n• Integrate LLMs and manage prompt optimization using LangChain, LangGraph, and Google ADK\\\\r\\\\n• Develop secure APIs (FastAPI/Flask) with observability and performance tuning\\\\r\\\\n• Implement and maintain MCP servers for orchestrating multi-agent workflows\\\\r\\\\n• Conduct A/B testing for LLM behavior, accuracy, and cost efficiency\\\\r\\\\n• Develop RESTful APIs (FastAPI/Flask) for LLM and agentic workflows\\\\r\\\\n• Implement authentication (OAuth2), logging, and observability\\\\r\\\\n• Optimize backend throughput for multi-agent systems\\\\r\\\\n• Provision and manage GCP infrastructure for the AI environment, MCP servers, and Streamlit UI\\\\r\\\\n• Implement infrastructure security aligned with DB CSO/CISO requirements and HCLTech patterns\\\\r\\\\n• Set up monitoring, alerting, and observability stacks (Prometheus, Grafana, Cloud Monitoring)\\\\r\\\\n• Manage service accounts, IAM policies, and network security for sandbox and production environments\\\\r\\\\n• Languages/Frameworks: Python, FastAPI, Flask\\\\r\\\\n• GenAI Frameworks: LangChain, LangGraph, Google ADK, Hugging Face\\\\r\\\\n• Databases: PostgreSQL, MongoDB, Redis, FAISS\\\\r\\\\n• Cloud: GCP (Vertex AI), Kubernetes\\\\r\\\\n• Agent Systems: MCP server, A2A communication\\\\r\\\\n

Key Responsibilities

Job Responsibilities : AI Engineer Engineer is expected to perform the following Services and/or provide the following deliverables: Agentic AI Engineer: • Lead design and implementation of multi-agent and RAG pipeline systems • Integrate LLMs and manage prompt optimization using LangChain, LangGraph, and Google ADK • Develop secure APIs (FastAPI/Flask) with observability and performance tuning • Implement and maintain MCP servers for orchestrating multi-agent workflows • Conduct A/B testing for LLM behavior, accuracy, and cost efficiency • Develop RESTful APIs (FastAPI/Flask) for LLM and agentic workflows • Implement authentication (OAuth2), logging, and observability • Optimize backend throughput for multi-agent systems • Provision and manage GCP infrastructure for the AI environment, MCP servers, and Streamlit UI • Implement infrastructure security aligned with DB CSO/CISO requirements and HCLTech patterns • Set up monitoring, alerting, and observability stacks (Prometheus, Grafana, Cloud Monitoring) • Manage service accounts, IAM policies, and network security for sandbox and production environments • Languages/Frameworks: Python, FastAPI, Flask • GenAI Frameworks: LangChain, LangGraph, Google ADK, Hugging Face • Databases: PostgreSQL, MongoDB, Redis, FAISS • Cloud: GCP (Vertex AI), Kubernetes • Agent Systems: MCP server, A2A communication

Skill Requirements

Skill Requirement : AI Engineer Engineer is expected to perform the following Services and/or provide the following deliverables: Agentic AI Engineer: • Lead design and implementation of multi-agent and RAG pipeline systems • Integrate LLMs and manage prompt optimization using LangChain, LangGraph, and Google ADK • Develop secure APIs (FastAPI/Flask) with observability and performance tuning • Implement and maintain MCP servers for orchestrating multi-agent workflows • Conduct A/B testing for LLM behavior, accuracy, and cost efficiency • Develop RESTful APIs (FastAPI/Flask) for LLM and agentic workflows • Implement authentication (OAuth2), logging, and observability • Optimize backend throughput for multi-agent systems • Provision and manage GCP infrastructure for the AI environment, MCP servers, and Streamlit UI • Implement infrastructure security aligned with DB CSO/CISO requirements and HCLTech patterns • Set up monitoring, alerting, and observability stacks (Prometheus, Grafana, Cloud Monitoring) • Manage service accounts, IAM policies, and network security for sandbox and production environments • Languages/Frameworks: Python, FastAPI, Flask • GenAI Frameworks: LangChain, LangGraph, Google ADK, Hugging Face • Databases: PostgreSQL, MongoDB, Redis, FAISS • Cloud: GCP (Vertex AI), Kubernetes • Agent Systems: MCP server, A2A communication

Other Requirements

Other Requirement : AI Engineer Engineer is expected to perform the following Services and/or provide the following deliverables: Agentic AI Engineer: • Lead design and implementation of multi-agent and RAG pipeline systems • Integrate LLMs and manage prompt optimization using LangChain, LangGraph, and Google ADK • Develop secure APIs (FastAPI/Flask) with observability and performance tuning • Implement and maintain MCP servers for orchestrating multi-agent workflows • Conduct A/B testing for LLM behavior, accuracy, and cost efficiency • Develop RESTful APIs (FastAPI/Flask) for LLM and agentic workflows • Implement authentication (OAuth2), logging, and observability • Optimize backend throughput for multi-agent systems • Provision and manage GCP infrastructure for the AI environment, MCP servers, and Streamlit UI • Implement infrastructure security aligned with DB CSO/CISO requirements and HCLTech patterns • Set up monitoring, alerting, and observability stacks (Prometheus, Grafana, Cloud Monitoring) • Manage service accounts, IAM policies, and network security for sandbox and production environments • Languages/Frameworks: Python, FastAPI, Flask • GenAI Frameworks: LangChain, LangGraph, Google ADK, Hugging Face • Databases: PostgreSQL, MongoDB, Redis, FAISS • Cloud: GCP (Vertex AI), Kubernetes • Agent Systems: MCP server, A2A communication
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Information at a Glance

Why HCLTech?

At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first.

HCLTech is a global technology company, home to more than 223,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending June 2026 totaled $14.8 billion.