Roles and Responsibilities Design, develop, test, deploy, and maintain complex SQL queries to meet business requirements. Optimize database performance by analyzing query execution plans using tools like Query Analyzer and SQL Profiler. Create stored procedures, views, triggers, CTEs, tables, indexes, normalized databases for efficient data storage. Collaborate with cross-functional teams to identify areas of improvement in existing systems. Desired Candidate Profile 1-2 years of experience in Microsoft SQL development with expertise in T-SQL programming language. Strong understanding of DDL/DML commands for creating/modifying database structures. Proficiency in writing complex queries including joins, aggregations, subqueries etc. . Experience with SSIS (SQL Server Integration Services) for extracting/transforming/loading data between different systems.

Job Summary: We are looking for an innovative and technically proficient AI Solution Designer to conceptualize, architect, and validate AI-based solutions, with a strong emphasis on Proof of Concept (PoC) development. The ideal candidate will translate business needs into AI-powered solutions, design architecture, guide development teams, and demonstrate feasibility through rapid prototyping and PoCs. Key Responsibilities: 1. Solution Design & Architecture Translate business problems into AI/ML-driven solution designs. Define scalable AI/ML solution architectures using appropriate models, frameworks, APIs, and data pipelines. Collaborate with data engineers, ML engineers, and business stakeholders to ensure alignment. 2. Proof of Concept Development Design and develop PoCs to validate feasibility, performance, and business value of proposed AI solutions. Rapidly prototype AI features using tools such as Python, TensorFlow, PyTorch, Hugging Face, OpenAI APIs, LangChain, etc. Document PoC findings, results, and recommendations for full-scale implementation. 3. Technical Analysis & Documentation Conduct deep technical evaluations of AI/ML models, including accuracy, performance, and risk. Create detailed technical documentation including solution architecture diagrams, data flow diagrams, and API specs. Present PoC results and technical recommendations to stakeholders and executive teams. 4. Stakeholder Engagement Engage with cross-functional teams (Product, IT, Business, and Data teams) to understand needs and gather requirements. Act as a bridge between data science, engineering, and business teams to ensure successful delivery of AI initiatives. 5. Innovation & Best Practices Stay updated with the latest trends in AI, including LLMs, Generative AI, and AutoML. Recommend tools, frameworks, and platforms that can accelerate PoC and solution delivery. Promote reusability and modular architecture principles for AI solution components. Design, develop, and deploy conversational AI solutions using various tools such as Wren AI, Voice Flow, and ML algorithms. Collaborate with cross-functional teams to integrate AI models into existing systems and applications. Develop generative AI models for text generation, image processing, and other forms of data manipulation. Troubleshoot issues related to AI model performance and optimize system architecture for improved efficiency. Required Skills & Experience: Proven experience in designing AI/ML solutions and delivering PoCs in a business context. Strong programming experience with Python; exposure to ML libraries like Scikit-learn, TensorFlow, PyTorch. Experience with cloud platforms (AWS, Azure, GCP) and AI services (SageMaker, Azure ML, Vertex AI). Hands-on with APIs and frameworks like OpenAI, LangChain, FastAPI, Flask. Familiarity with MLOps tools (MLflow, DVC, Kubeflow) is a plus. Strong problem-solving, analytical, and architectural thinking abilities. Excellent communication and stakeholder management skills. Preferred Qualifications: Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field. Certifications in AI/ML technologies or cloud platforms. Experience with enterprise AI systems or AI product development lifecycle. 3-6 years of experience in Conversational AI development or a related field. Strong understanding of AIMl, Python programming language, TensorFlow/PyTorch machine learning frameworks. Experience with natural language processing (NLP) techniques including large language models like BERT.