Senior AI Engineer

HCLTech; England; On-site; GBP 70,000 - 90,000; Full time

Job summary

A global technology company in the UK is seeking a Senior AI Developer to focus on innovative AI solutions within the financial sector. The ideal candidate will have 6–8 years of experience, expertise in frameworks such as LangChain, and proficiency in Python and GCP. Responsibilities include architecting AI agents, optimizing RAG systems, and managing cloud-native applications. This position offers a chance to drive technological innovation in a vibrant work environment.

Qualifications

• 6-8 years of hands-on experience in software engineering with AI/ML focus.
• Proven track record of delivering high-performance software solutions.
• Experience in deploying complex AI/ML systems.

Responsibilities

• Design and implement goal-driven AI agents using frameworks.
• Engineer and optimize RAG systems for data processing.
• Build and manage scalable AI applications on GCP.

Skills

Agentic Frameworks

Prompt Engineering

Python

GCP

FastAPI

Education

Bachelor's or Master's degree in Computer Science, AI, Machine Learning

Tools

Google Kubernetes Engine

PostgreSQL

Docker

Job description

HCLTech is a global technology company, home to 219,000+ people across 54 countries, delivering industry-leading capabilities centered on digital, engineering and cloud, 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 $13+ billion.

Senior AI Developer (Agentic Experience)

Role Summary

It is crucial that the candidates have experience with the Agentic AI framework, specifically with tools like LangGraph and Google ADK. These skills are essential for the role.

We are seeking an experienced Senior AI Developer. This role focuses on the design, development, and deployment of intelligent autonomous systems that can reason, plan, and execute complex tasks. The ideal candidate will build production‑grade AI solutions that drive significant innovation and efficiency within our global financial services operations.

Core Responsibilities

• Architect Autonomous Agents : Design and implement robust, goal‑driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK).
• Develop and Evaluate RAG Pipelines : Engineer and optimize end‑to‑end Retrieval‑Augmented Generation (RAG) systems, including data ingestion, chunking strategies, and implementing rigorous pipeline evaluation frameworks for accuracy and performance.
• Fine‑Tune & Optimize LLMs : Implement advanced model customization techniques, including PEFT (Parameter‑Efficient Fine‑Tuning) and QLoRA, to adapt large language models for specialized financial tasks.
• Deploy Cloud‑Native AI Solutions : Build, deploy, and manage scalable AI applications on the Google Cloud Platform (GCP), with a strong focus on Vertex AI services.
• Build Scalable Backend Services : Develop secure, high‑performance APIs and microservices using Python and FastAPI to integrate agentic systems into the wider enterprise architecture.

Technical Skillset Requirements

Core AI & Frameworks

• Agentic Frameworks : Expert‑level knowledge of agentic frameworks such as LangChain, LangGraph, Google Agent Development Kit (ADK)
• LLM Expertise : Advanced Prompt Engineering and hands‑on experience with model fine‑tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3.
• RAG & Vector Databases : Deep expertise in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma.

Software & Cloud Engineering

• Programming & APIs : Expert‑level Python and demonstrable experience building production services with FastAPI.
• Cloud Platform : Mastery of GCP, particularly Vertex AI, Google Kubernetes Engine (GKE), and Cloud Functions.
• Databases : Strong command of relational databases like PostgreSQL and familiarity with NoSQL solutions.
• MLOps & DevOps : Production experience with Docker, Kubernetes, CI / CD pipelines (e.g., Jenkins, GitHub Actions), and Infrastructure as Code (Terraform).

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field.
• 6-8 years of professional, hands‑on experience in software engineering, with a primary focus on building and deploying complex AI / ML systems into production.
• A proven track record of delivering scalable, reliable, and high‑performance software solutions from concept to deployment.

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