AI Engineer, Agentic AI, Python, Pycharm, LLM, Agentic
New Yesterday
AI Engineer
with deep expertise in
Agentic AI, Large Language Models, NLP, GenAI pipelines, cloud ML platforms, and vector-based retrieval systems . This is an opportunity to join an advanced AI team building next-generation intelligent systems, multi-agent applications, and high-scale GenAI microservices. You will design, deploy, and optimise production-grade AI/ML systems powering millions of customer interactions. You will work across
Python, cloud-native architectures, vector search, RAG frameworks, orchestration engines, and multi-agent systems , shaping AI capabilities that transform how organisations interact, automate, and understand their customers.
Key Responsibilities
AI / LLM / Agentic Engineering
Design, build, and optimise
agentic AI systems
using frameworks such as
LangChain, LangGraph, Vertex AI Agent Builder, Bedrock Agents, AgentKit, CrewAI , and custom orchestration. Build LLM-powered applications using models including
GPT-4o/5, Llama3, Claude, Gemini 2.5 Pro, Bard , and enterprise-grade LLM deployments. Implement
RAG
and
CAG
architectures using
Pinecone, OpenSearch, Google GenAI Search , and custom vector stores. Engineer
domain-tuned embeddings
using ADA-002, Gecko, Word2Vec, BERT, Sentence Encoder, and topic modelling.
AI/ML Pipelines & MLOps
Develop scalable
AI/ML microservices
using Docker, Kubernetes (EKS/GKE), and CI/CD-driven automation. Build and enhance pipelines for
model evaluation, bias/drift detection, real-time inference, and monitoring . Optimise inference latency for high-volume, near-real-time applications such as transcript and behavioural analysis.
NLP & Applied Machine Learning
Apply text clustering, N-gram analytics, sentiment modelling, intent classification, and summarisation for insight extraction. Refine conversational intent taxonomies and behavioural models for more accurate AI assistant interactions.
Data Engineering & Cloud Integration
Use cloud services including
SageMaker, Azure ML Studio, Vertex AI
for training, deployment, and monitoring. Manage datasets using
GCP Cloud Storage
and implement secure, compliant data workflows.
AI Governance & Quality Assurance
Establish guardrails, safety layers, automated evaluation frameworks, and prompt governance patterns. Ensure all AI systems meet stringent
data governance, privacy, and financial-sector compliance
requirements.
Technical Skills
Languages & Development
Python, Java, SQL, Shell Scripting, Node.js, Streamlit IDE experience:
PyCharm, VS Code, JupyterLab, Eclipse, Notepad++, Sagemaker Studio, Azure ML Studio, Vertex AI Workbench
Python Libraries
NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras, PyTorch, PySpark, SpaCy, SciPy, NLTK, Statsmodels, Boto3, AzureSDK
NLP & LLMs
BERT, Word2Vec, Universal Sentence Encoder, NLTK, embeddings, fuzzy matching, topic modelling LLM experience: GPT-3.5/4o/5, Llama2/3, Claude, Gemini, Bedrock models, SQuAD fine-tuning, custom RAG agents
AI Search & Vector Innovations
Pinecone, OpenSearch, LangChain/LangGraph, LangIndex, Vertex AI Search, Vector DBs, RAG pipelines
What We're Looking For
Proven experience developing production-grade
LLM, GenAI, NLP, or agent-based AI systems . Strong engineering foundation across Python, cloud platforms, APIs, and vector search. Experience with complex multi-agent AI orchestration. Ability to deliver high-scale, low-latency AI solutions in demanding environments. Strong collaboration, architectural thinking, and a passion for cutting-edge AI innovation.
TPBN1_UKTJ
- Location:
- United Kingdom
- Job Type:
- FullTime
- Category:
- IT;IT