Job
Description
Bison Global Search is seeking a Principal AI Engineer for a leading product company in Chennai . They work on some cutting-edge technologies in the BIOS Industry Please find below details about the role : Location: Chennai (please do not apply if you are not willing to relocate to Chennai) Company: Product Company (Leader in BIOS Products) Designation: Principal AI Engineer Skills Required: Python + RAG ( Retrieval-Augmented Generation) + Agentic AI Experience : 8 + years of experience as an AI engineer + Guiding and mentor a team of 3-5 AI engineers Please find below the complete JD. if this interests you, please apply to this email We are looking for a highly skilled Principal AI Engineer with deep expertise in Retrieval-Augmented Generation (RAG) and Agentic AI to lead our AI initiatives and drive innovation in FW and Data Center AI solutions . This role requires a strategic thinker who can design and deploy scalable AI architectures, integrate LLMs with retrieval-based techniques, and develop intelligent agentic systems that autonomously interact with data, APIs, and workflows. This role will lead the design and deployment of cutting-edge AI-driven solutions, focusing on LLMs for code synthesis, automated testing, and intelligent autonomous agents that enhance software development workflows with strong technical expertise, strategic vision, and leadership to build and deploy AI-driven products that align with business goals. Key Responsibilities: AI Strategy and Leadership: Define and execute AI strategies focused on RAG-based retrieval, code generation, and AI-assisted software engineering Work with stakeholders to align AI capabilities with business objectives and software development needs Research and integrate cutting-edge LLMs and autonomous AI agent architecture into development processes. RAG & Agentic AI Development: Develop RAG pipelines that enhance AI‘s ability to retrieve relevant knowledge and generate context-aware responses. Build and optimize agentic AI systems that can interact with APIs, databases, and development environments (such as LangChain, OpenAI APIs, etc.) Implement AI-powered search, chatbots, and decision-support tools for software engineers. Fine-tune LLMs (GPT, Llama, Mistral, Claude, Gemini etc.) for domain-specific applications. Optimize retrieval mechanisms to enhance response accuracy, grounding AI outputs in real-world data Code generation & Test case Automation: Leverage LLMs to generate high-quality, production-ready code Develop AI-driven test case generation tools that automatically create and validate unit tests, integration tests, and regression tests Integrate AI-driven code assistants and programming agents into IDE and CI/CD workflows Optimize prompt engineering and fine-tuning strategies for LLMs to improve code quality and efficiency MLOps & Scalable AI Systems: Architect and deploy scalable AI models and retrieval pipelines using cloud-based MLOps pipelines (AWS/GCP/Azure, Docker, Kubernetes) Optimize LLMs for real-time AI inferencing , ensuring low latency and high-performance AI solutions. Collaboration: Work cross-functionally with product teams, software engineers, and business stakeholders to integrate AI solutions into products. Mentorship: Guide and mentor a team of 3-5 AI engineers in LLM fine-tuning, retrieval augmentation, and autonomous AI agents. Establish best practices for AI-assisted software development, secure AI integration, and bias mitigation. Research & Innovation: Commitment to staying updated with the latest AI and machine learning research and advancements . Ability to think creatively and propose innovative solutions to complex problems. Model Development: Ability to design, train, and evaluate various AI models , including LLMs and standalone models —familiarity with model training tools and frameworks like Hugging Face Trainer, Fairseq, etc . Required Qualifications: Education: Master's or Ph.D. in Computer Science, AI, Machine Learning, or a related field. Experience: 8+ years of experience in AI and machine learning, with at least 2 years of experience working on LLMs, code generation, RAG, or AI-powered automation . Technical skills: Proficiency in Python, Tensorflow, PyTorch, and LangChain Experience with LLM fine-tuning for code generation Strong expertise in vector databases (FAISS, Weaviate, Chroma, Pinecone, Milvus) and retrieval models Hands-on experience with AI-powered code assistants (Copilot, code Llama, Codex, GTP-4) Knowledge of automated software testing, AI-driven test case generation, AI-assisted debugging Experience with multi-agent AI systems (LangGraph, CrewAI, AutgoGen, OpenAI Assistants API) for autonomous coding tasks Knowledge of GoLang for building high-performance and scalable components and unit test case generation using CMocka is a plus. Hands-on model development, working with business stakeholders to define KPIs and develop and deliver multi-modal (Text and Images) and ensemble models. Develop novel approaches to solve firmware lifecycle management code generation and customer support issues. Implement advanced natural language processing and computer vision models to extract insights from diverse data sources , user-generated data, and images. Automate model lifecycle management . Stay updated with AI and machine learning technology advancements to drive Firmware Lifecycle Management. Analytical & Problem-Solving: Analytical Thinking: Strong analytical skills to interpret complex data and derive actionable insights. Problem-Solving: Ability to troubleshoot and resolve technical issues related to AI models and systems. Research & Innovation: Continuous Learning: Commitment to staying updated with the latest research and advancements in AI and machine learning. Innovation: Ability to think creatively and propose innovative solutions to complex problems. Soft Skills: Communication: Excellent verbal and written communication skills. Adaptability: Ability to adapt to changing technologies and project requirements. Team Player: Strong interpersonal skills and the ability to work well in a team environment. Preferred Qualifications: Experience with deploying and maintaining AI models in production environments . Familiarity with RAG-specific techniques like knowledge distillation or multi-hop retrieval . Understanding of reinforcement learning and active learning techniques for model improvement . Previous experience with large-scale NLP systems and AI-powered search engines . Contribution to AI research, patents, or open-source development Show more Show less
