AI Engineer, Agentic AI, Python, Pycharm, LLM, Agentic

We are seeking a highly skilled 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.

• Design, build, and optimise agentic AI systems using frameworks such as Lang Chain, Lang Graph, Vertex AI Agent Builder, Bedrock Agents, Agent Kit, 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, Open Search, Google GenAI Search
  , and custom vector stores.
• Engineer domain‑tuned embeddings using ADA‑002, Gecko, Word2
  
  Vec, BERT, Sentence Encoder, and topic modelling.
• 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.
• 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.
• Use cloud services including Sage Maker, Azure ML Studio, Vertex AI for training, deployment, and monitoring.
• Manage datasets using GCP Cloud Storage and implement secure, compliant data workflows.

• 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.

• BERT, Word2
  
  Vec, Universal Sentence Encoder, NLTK, embeddings, fuzzy matching, topic modelling

• 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.