AI Engineer

The core responsibilities for the job include the following:
Solution Architecture and Deployment:
Design and deploy scalable, secure GenAI architectures integrated into customer-facing products.
Build REST APIs for AI/ML models and deploy them in containerised environments (Docker, Kubernetes) on cloud platforms (AWS, Azure, GCP).
GenAI and LLM Development:
Fine-tune and optimise generative models, including GPT, VAEs, GANs, and transformer-based architectures.
Apply techniques like Retrieval-Augmented Generation (RAG) and prompt engineering to enhance model performance and relevance.
Work with both commercial and open-source LLMs (e. g., GPT-4 Claude, LLaMA 3.2 Phi).
Agentic AI Integration:
Primary Focus: Build, deploy, and optimise AI agents leveraging frameworks such as LangChain, LangGraph, CrewAI, AgentFlow, and Autogen.
Implement orchestration strategies, multi-agent collaboration, tool integration, and memory/state management.
Drive experimentation to create autonomous or semi-autonomous agents that solve real business workflows and decision-making processes.
MLOps and Performance Optimisation:
Establish MLOps pipelines covering model lifecycle: training, CI/CD, monitoring, and retraining.
Use tools like Git, Docker, Kubernetes, and vector DBs to ensure efficient and reliable deployment.
Optimise resource utilisation and infrastructure costs.
Cross-Functional Collaboration:
Partner with engineering, data science, and product teams to align technical solutions with business goals.
Effectively communicate complex concepts across diverse technical and non-technical audiences.
Stay current with industry advancements and drive innovation in GenAI and AI agent strategy.
Requirements:
Strong proficiency in Python, SQL, and GenAI frameworks (e. g., LangChain).
Hands-on experience in building and deploying AI agents with orchestration, tool use, and state management.
In-depth knowledge of LLM architecture, RAGs, embeddings, prompt tuning, vector databases, agentic AI patterns (ReAct, tool-calling agents, multi-step reasoning, guardrails)
Experience with cloud platforms (AWS, Azure, GCP) and containerisation.
Strong analytical, problem-solving, and communication skills.
Data integration experience with REST APIs, Google APIs, and SQL databases. Comfortable moving data between systems.
Experience in Web development: FastAPIs, Typescript, async patterns, building production APIs, React, node.js, Component architecture, hooks, state management, consuming streaming APIs (SSE/WebSocket)
Preferred:
3+ years of hands-on experience with LLMs and GenAI in production settings.
Exposure to agentic AI tools and multi-agent workflows (e. g., CrewAI, LangGraph, and Autogen).
Familiarity with MLOps and AI deployment best practices.
Experience in client-facing or cross-functional AI initiatives.
Publications, open-source contributions, or demonstrable projects showcasing AI agent development.

Experience
3-6 yrs