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.

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.
• 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 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 Libraries

NLP & LLMs

• BERT, Word2Vec, Universal Sentence Encoder, NLTK, embeddings, fuzzy matching, topic modelling

AI Search & Vector Innovations

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.

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