Job SummaryWe are looking for a hands-on AI Engineer to design, develop, integrate, and deploy Generative AI, AI chatbot, and Agentic AI solutions. The role will focus on building practical AI applications and integrating LLM capabilities with existing applications, APIs, databases, and enterprise systems.Key ResponsibilitiesDesign and develop AI chatbots, copilots, and LLM-powered applications.Build Agentic AI solutions with reasoning, tool/function calling, multi-step task execution, and workflow automation.Implement RAG, embeddings, vector search, prompt engineering, and conversational memory.Work with commercial and open-source LLMs and select appropriate models based on business and technical requirements.Integrate AI solutions with REST APIs, databases, CRM/ERP systems, and enterprise applications.Develop AI integration services and APIs for existing systems and business workflows.Evaluate and optimize AI solutions for accuracy, reliability, latency, scalability, security, and cost.Collaborate with software engineers, architects, product teams, and business stakeholders.Keep up to date with developments in Generative AI, LLMs, Agentic AI, and AI automation.RequirementsBachelor's degree in Computer Science, AI/ML, Software Engineering, or a related field.3+ years of experience in AI/ML engineering, software engineering, or a related role.Hands-on experience developing LLM applications, AI chatbots, or Generative AI solutions.Practical experience with AI Agents, Agentic AI, tool/function calling, or AI workflow automation.Experience with open-source LLMs such as Llama, Qwen, Mistral, Gemma, or equivalent.Strong Python programming and software engineering skills.Experience with REST APIs, system integration, databases, and cloud/on-premise environments.Hands-on experience with RAG, vector databases, embeddings, and prompt engineering.Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or equivalent.Experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic, Gemini, and/or open-source models.Familiarity with Docker, Git, CI/CD, and production deployment.Preferred SkillsExperience serving open-source LLMs using vLLM, Hugging Face, Ollama, or equivalent.Experience with MCP, multi-agent systems, LLM evaluation/observability, fine-tuning, LoRA/QLoRA, or model optimization.Experience with AWS, Azure, or Google Cloud.Knowledge of AI security, data privacy, access control, and responsible AI.
Develop project plans, track milestones, and ensure timely delivery of initiatives within scope, timeline, and resources.
Support the implementation and enhancement of warehouse systems (e.g., WMS tools, automation workflows, operational dashboards) to improve fulfillment performance.
Work closely with ground operations teams to validate solutions, conduct testing, and ensure smooth rollout and adoption of new tools or processes.
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Perform essential laboratory testing including MFI, density and moisture analysis, dispersion and pellet quality evaluation, physical and visual inspection, formulation/component verification, as well as other relevant polymer/material characterisation.
Analyse test results and recommend formulation or process improvements.
Support production trials, scale-up and process parameter optimisation.
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Troubleshooting s Risk Mitigation: Identify and resolve technical bottlenecks during migration/deployment phases, ensuring minimal disruption to client operations.
Documentation s Handoff: Build standard operating procedures (SOPs), Project As-Builts, Low-Level Diagram(LLD), system architecture diagrams, and detailed handover documentation for ongoing support teams.
Process Optimization: Continuously refine deployment scripts, templates, and rollout checklists to improve implementation speed and consistency.
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