Collaborate closely with Product Owners, business users, internal departments, and external customers to understand business requirements and translate them into scalable and practical software solutions.
Ensure software delivery aligns with product goals, user experience, operational requirements, and customer expectations.
Define and maintain software development standards, architecture guidelines, coding practices, documentation standards, and security best practices.
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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.
Translate complex algorithmic ideas - syntax parsing, semantic chunking, custom routing - into reliable, production ready software components.
Build and rigorously evaluate prototypes for future AI initiatives, including open source small language models (SLMs), custom embeddings, and multi agent / cross validation patterns, backed by measurable retrieval and quality metrics.
Architect multi tenant data, identity, and authorization layers that meet enterprise security and compliance requirements.
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Technical Leadership & Solution Ownership: Lead the technical direction of AI initiatives by driving architecture decisions, establishing best practices, mentoring team members, and guiding the end-to-end delivery of scalable AI solutions.
Collaboration & Delivery: Work with Product Owner, Developers, Quality Engineers and UX Designers to deliver the Virtual Companion. Contribute to sprint planning, code reviews and documentation.
Bachelor’s degree or above, with 5-8 years in software development and relevant experience in leading AI/ML development project
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Assess the commercial potential and technical readiness levels of internal R&D projects for potential spin-offs or new business incubation.
Bachelor's degree in Engineering (e.g., Electrical, Mechanical, Chemical, Software, Materials Science, Biomedical) or a relevant scientific discipline from an accredited institution.
Master's or Ph.D. in a relevant engineering or scientific field is highly preferred.
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