Job
Description
AI Engineer Key Responsibilities GenAI Development: Design and develop advanced GenAI models (e.g., LLMs, DALL E models) and AI Agents to automate internal tasks and workflows. Exposure to LLMs: Utilize Azure Open AI APIs, experience on models like GPT4o, O3 , llama3 Enhance the existing RAG based application: In depth understanding of stages of RAG - chunking, retrieval etc. Cloud Deployment: Deploy and scale GenAI solutions on Azure Cloud services (e.g., Azure Function App) for optimal performance. In depth understanding of ML models like linear regression, random forest, decision trees. In depth understanding on clustering and supervised models. AI Agent Development: Build AI agents using frameworks like LangChain to streamline internal processes and boost efficiency. Data Analytics: Perform advanced data analytics to preprocess datasets, evaluate model performance, and derive actionable insights for GenAI solutions. Data Visualization: Create compelling visualizations (e.g., dashboards, charts) to communicate model outputs, performance metrics, and business insights to stakeholders. Stakeholder Collaboration: Partner with departments to gather requirements, align on goals, and present technical solutions and insights effectively to non-technical stakeholders. Model Optimization: Fine-tune GenAI models for efficiency and accuracy using techniques like prompt engineering, quantization, and RAG (Retrieval-Augmented Generation). LLMOps Best Practices: Implement GenAI-specific MLOps, including CI/CD pipelines (Git, Azure DevOps) Leadership: Guide cross-functional teams, mentor junior engineers, and drive project execution with strategic vision and ownership. Helicopters, strategic Thinking**: Develop innovative GenAI strategies to address business challenges, leveraging data insights to align solutions with organizational goals. Self-Driven Execution: Independently lead projects to completion with minimal supervision, proactively resolving challenges and seeking collaboration when needed. Continuous Learning: Stay ahead of GenAI, analytics, and visualization advancements, self-learning new techniques to enhance project outcomes. Required Skills & Experience Experience: 8-12 years in AI/ML development, with at least 4 years focused on Generative AI and AI agent frameworks. Education: BTech/BE in Computer Science, Engineering, or equivalent (Master’s or PhD in AI/ML is a plus). Programming: Expert-level Python proficiency, with deep expertise in GenAI libraries (e.g., LangChain, Hugging Face Transformers, PyTorch, Open AI SDK) and data analytics libraries (e.g., Pandas, NumPy), sk-learn. Mac Data Analytics: Strong experience in data preprocessing, statistical analysis, and model evaluation to support GenAI development and business insights. Data Visualization: Proficiency in visualization tools (e.g., Matplotlib, Seaborn, Plotly, Power BI, or Tableau) to create dashboards and reports for stakeholders. Azure Cloud Expertise: Strong experience with Azure Cloud services (e.g., Azure Function App, Azure ML, serverless) for model training and deployment. GenAI Methodologies: Deep expertise in LLMs, AI agent frameworks, prompt engineering, and RAG for internal workflow automation. Deployment: Proficiency in Docker, Kubernetes, and CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for production-grade GenAI systems. LLMOps: Expertise in GenAI MLOps, including experiment tracking (e.g., Weights & Biases), automated evaluation metrics (e.g., BLEU, ROUGE), and monitoring. Soft Skills: Communication: Exceptional verbal and written skills to articulate complex GenAI concepts, analytics, and visualizations to technical and non-technical stakeholders. Strategic Thinking: Ability to align AI solutions with business objectives, using data-driven insights to anticipate challenges and propose long-term strategies. Problem-Solving: Strong analytical skills with a proactive, self-starter mindset to independently resolve complex issues. Collaboration: Collaborative mindset to work effectively across departments and engage colleagues for solutions when needed. Speed to outcome Preferred Skills Experience deploying GenAI models in production environments, preferably on Azure Familiarity with multi-agent systems, reinforcement learning, or distributed training (e.g., DeepSpeek). Knowledge of DevOps practices, including Git, CI/CD, and infrastructure-as-code. Advanced data analytics techniques (e.g., time-series analysis, A/B testing) for GenAI applications. Experience with interactive visualization frameworks (e.g., Dash, Streamlit) for real-time dashboards. Contributions to GenAI or data analytics open-source projects or publications in NLP, generative modeling, or data scien Show more Show less
