Assistant Vice President

Job Description: AI Engineer (12-15 Years Experience) Position Title Senior AI Engineer / Lead AI Engineer Experience 12-15 Years Location Flexible / Hybrid / Onsite Job Summary We are seeking an experienced AI Engineer with 12-15 years of overall experience in Data Engineering, Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will have a strong background in designing, developing, and deploying scalable AI solutions leveraging Large Language Models (LLMs), NLP, Deep Learning, and cloud-based AI platforms. This role requires a blend of technical leadership, solution architecture, stakeholder management, and hands-on development expertise. Key Responsibilities AI & Generative AI Development • Design and develop enterprise-grade AI and Generative AI applications. • Build and deploy AI-powered assistants, copilots, chatbots, and intelligent automation solutions. • Develop Agentic AI frameworks capable of autonomous reasoning, planning, and task execution. • Implement Retrieval Augmented Generation (RAG) architectures for enterprise knowledge management. • Fine-tune, evaluate, and optimize foundation models and LLMs. Large Language Models (LLMs) • Work with commercial and open-source LLMs including: • Claude • GPT Series • Llama • Mistral • Gemma • Falcon • Develop prompt engineering strategies and model evaluation frameworks. • Optimize inference performance, latency, and cost for production deployments. Natural Language Processing (NLP) • Build advanced NLP solutions including: • Text Classification • Sentiment Analysis • Named Entity Recognition (NER) • Text Summarization • Question Answering Systems • Semantic Search • Develop multilingual AI solutions. Data Engineering & AI Data Pipelines • Design scalable data ingestion, transformation, and feature engineering pipelines. • Build AI-ready data platforms using structured and unstructured data. • Integrate data from multiple enterprise systems and external sources. • Ensure data quality, governance, security, and compliance. MLOps & AI Operations • Develop CI/CD pipelines for AI and ML workloads. • Implement model monitoring, observability, retraining, and governance frameworks. • Automate AI model deployment and lifecycle management. • Establish best practices for AI reliability, explainability, and responsible AI. Architecture & Leadership • Lead AI solution architecture discussions and design reviews. • Mentor Data Scientists, ML Engineers, and AI Developers. • Collaborate with business stakeholders to identify AI use cases and deliver measurable business value. • Drive AI innovation initiatives and proof-of-concepts. Required Technical Skills AI & Machine Learning • Machine Learning • Deep Learning • Neural Networks • Generative AI • Agentic AI • Reinforcement Learning • Computer Vision (Good to Have) LLM & GenAI Frameworks • LangChain • LangGraph • LlamaIndex • Semantic Kernel • AutoGen • CrewAI • Haystack Programming Languages • Python (Expert) • SQL • PySpark • Java/Scala (Preferred) NLP Technologies • Transformers • Hugging Face • BERT • SpaCy • NLTK Data Engineering • Databricks • Apache Spark • Delta Lake • Azure Data Factory • Snowflake • Data Warehousing Concepts Cloud Platforms • Microsoft Azure • AWS • Google Cloud Platform (Preferred) MLOps & DevOps • MLflow • Kubeflow • Docker • Kubernetes • Git • Azure DevOps • GitHub Actions Vector Databases • Pinecone • Weaviate • FAISS • ChromaDB • Azure AI Search Mandatory Qualifications • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field. • 12-15 years of overall IT experience. • Minimum 5+ years in AI/ML development. • Minimum 3+ years of hands-on experience in Generative AI and Large Language Models. • Experience delivering AI solutions in production environments. • Strong expertise in Python, Data Engineering, and Cloud Platforms. Preferred Certifications • Microsoft Certified: Azure AI Engineer Associate (AI-102) • Microsoft Certified: Azure Data Engineer Associate (DP-203) • Databricks Generative AI Certification • AWS Certified Machine Learning Specialty • Google Professional Machine Learning Engineer Soft Skills • Strong communication and presentation skills. • Excellent stakeholder management capabilities. • Ability to translate business requirements into AI solutions. • Team leadership and mentoring experience. • Strategic thinking and problem-solving skills. Key Deliverables • Enterprise AI Solutions • Generative AI Applications • Agentic AI Frameworks • RAG-Based Knowledge Systems • LLM-Powered Business Applications • AI Governance & MLOps Frameworks • Production-Ready Machine Learning Solutions This role is ideal for a seasoned AI professional capable of leading enterprise-wide AI transformation initiatives while remaining hands-on with modern Generative AI, Agentic AI, NLP, Data Engineering, and LLM technologies. Job Description: AI Engineer (12-15 Years Experience) Position Title Senior AI Engineer / Lead AI Engineer Experience 12-15 Years Location Flexible / Hybrid / Onsite Job Summary We are seeking an experienced AI Engineer with 12-15 years of overall experience in Data Engineering, Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will have a strong background in designing, developing, and deploying scalable AI solutions leveraging Large Language Models (LLMs), NLP, Deep Learning, and cloud-based AI platforms. This role requires a blend of technical leadership, solution architecture, stakeholder management, and hands-on development expertise. Key Responsibilities AI & Generative AI Development • Design and develop enterprise-grade AI and Generative AI applications. • Build and deploy AI-powered assistants, copilots, chatbots, and intelligent automation solutions. • Develop Agentic AI frameworks capable of autonomous reasoning, planning, and task execution. • Implement Retrieval Augmented Generation (RAG) architectures for enterprise knowledge management. • Fine-tune, evaluate, and optimize foundation models and LLMs. Large Language Models (LLMs) • Work with commercial and open-source LLMs including: • Claude • GPT Series • Llama • Mistral • Gemma • Falcon • Develop prompt engineering strategies and model evaluation frameworks. • Optimize inference performance, latency, and cost for production deployments. Natural Language Processing (NLP) • Build advanced NLP solutions including: • Text Classification • Sentiment Analysis • Named Entity Recognition (NER) • Text Summarization • Question Answering Systems • Semantic Search • Develop multilingual AI solutions. Data Engineering & AI Data Pipelines • Design scalable data ingestion, transformation, and feature engineering pipelines. • Build AI-ready data platforms using structured and unstructured data. • Integrate data from multiple enterprise systems and external sources. • Ensure data quality, governance, security, and compliance. MLOps & AI Operations • Develop CI/CD pipelines for AI and ML workloads. • Implement model monitoring, observability, retraining, and governance frameworks. • Automate AI model deployment and lifecycle management. • Establish best practices for AI reliability, explainability, and responsible AI. Architecture & Leadership • Lead AI solution architecture discussions and design reviews. • Mentor Data Scientists, ML Engineers, and AI Developers. • Collaborate with business stakeholders to identify AI use cases and deliver measurable business value. • Drive AI innovation initiatives and proof-of-concepts. Required Technical Skills AI & Machine Learning • Machine Learning • Deep Learning • Neural Networks • Generative AI • Agentic AI • Reinforcement Learning • Computer Vision (Good to Have) LLM & GenAI Frameworks • LangChain • LangGraph • LlamaIndex • Semantic Kernel • AutoGen • CrewAI • Haystack Programming Languages • Python (Expert) • SQL • PySpark • Java/Scala (Preferred) NLP Technologies • Transformers • Hugging Face • BERT • SpaCy • NLTK Data Engineering • Databricks • Apache Spark • Delta Lake • Azure Data Factory • Snowflake • Data Warehousing Concepts Cloud Platforms • Microsoft Azure • AWS • Google Cloud Platform (Preferred) MLOps & DevOps • MLflow • Kubeflow • Docker • Kubernetes • Git • Azure DevOps • GitHub Actions Vector Databases • Pinecone • Weaviate • FAISS • ChromaDB • Azure AI Search Mandatory Qualifications • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field. • 12-15 years of overall IT experience. • Minimum 5+ years in AI/ML development. • Minimum 3+ years of hands-on experience in Generative AI and Large Language Models. • Experience delivering AI solutions in production environments. • Strong expertise in Python, Data Engineering, and Cloud Platforms. Preferred Certifications • Microsoft Certified: Azure AI Engineer Associate (AI-102) • Microsoft Certified: Azure Data Engineer Associate (DP-203) • Databricks Generative AI Certification • AWS Certified Machine Learning Specialty • Google Professional Machine Learning Engineer Soft Skills • Strong communication and presentation skills. • Excellent stakeholder management capabilities. • Ability to translate business requirements into AI solutions. • Team leadership and mentoring experience. • Strategic thinking and problem-solving skills. Key Deliverables • Enterprise AI Solutions • Generative AI Applications • Agentic AI Frameworks • RAG-Based Knowledge Systems • LLM-Powered Business Applications • AI Governance & MLOps Frameworks • Production-Ready Machine Learning Solutions This role is ideal for a seasoned AI professional capable of leading enterprise-wide AI transformation initiatives while remaining hands-on with modern Generative AI, Agentic AI, NLP, Data Engineering, and LLM technologies. Job Description: AI Engineer (12-15 Years Experience) Position Title Senior AI Engineer / Lead AI Engineer Experience 12-15 Years Location Flexible / Hybrid / Onsite Job Summary We are seeking an experienced AI Engineer with 12-15 years of overall experience in Data Engineering, Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will have a strong background in designing, developing, and deploying scalable AI solutions leveraging Large Language Models (LLMs), NLP, Deep Learning, and cloud-based AI platforms. This role requires a blend of technical leadership, solution architecture, stakeholder management, and hands-on development expertise. Key Responsibilities AI & Generative AI Development • Design and develop enterprise-grade AI and Generative AI applications. • Build and deploy AI-powered assistants, copilots, chatbots, and intelligent automation solutions. • Develop Agentic AI frameworks capable of autonomous reasoning, planning, and task execution. • Implement Retrieval Augmented Generation (RAG) architectures for enterprise knowledge management. • Fine-tune, evaluate, and optimize foundation models and LLMs. Large Language Models (LLMs) • Work with commercial and open-source LLMs including: • Claude • GPT Series • Llama • Mistral • Gemma • Falcon • Develop prompt engineering strategies and model evaluation frameworks. • Optimize inference performance, latency, and cost for production deployments. Natural Language Processing (NLP) • Build advanced NLP solutions including: • Text Classification • Sentiment Analysis • Named Entity Recognition (NER) • Text Summarization • Question Answering Systems • Semantic Search • Develop multilingual AI solutions. Data Engineering & AI Data Pipelines • Design scalable data ingestion, transformation, and feature engineering pipelines. • Build AI-ready data platforms using structured and unstructured data. • Integrate data from multiple enterprise systems and external sources. • Ensure data quality, governance, security, and compliance. MLOps & AI Operations • Develop CI/CD pipelines for AI and ML workloads. • Implement model monitoring, observability, retraining, and governance frameworks. • Automate AI model deployment and lifecycle management. • Establish best practices for AI reliability, explainability, and responsible AI. Architecture & Leadership • Lead AI solution architecture discussions and design reviews. • Mentor Data Scientists, ML Engineers, and AI Developers. • Collaborate with business stakeholders to identify AI use cases and deliver measurable business value. • Drive AI innovation initiatives and proof-of-concepts. Required Technical Skills AI & Machine Learning • Machine Learning • Deep Learning • Neural Networks • Generative AI • Agentic AI • Reinforcement Learning • Computer Vision (Good to Have) LLM & GenAI Frameworks • LangChain • LangGraph • LlamaIndex • Semantic Kernel • AutoGen • CrewAI • Haystack Programming Languages • Python (Expert) • SQL • PySpark • Java/Scala (Preferred) NLP Technologies • Transformers • Hugging Face • BERT • SpaCy • NLTK Data Engineering • Databricks • Apache Spark • Delta Lake • Azure Data Factory • Snowflake • Data Warehousing Concepts Cloud Platforms • Microsoft Azure • AWS • Google Cloud Platform (Preferred) MLOps & DevOps • MLflow • Kubeflow • Docker • Kubernetes • Git • Azure DevOps • GitHub Actions Vector Databases • Pinecone • Weaviate • FAISS • ChromaDB • Azure AI Search Mandatory Qualifications • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field. • 12-15 years of overall IT experience. • Minimum 5+ years in AI/ML development. • Minimum 3+ years of hands-on experience in Generative AI and Large Language Models. • Experience delivering AI solutions in production environments. • Strong expertise in Python, Data Engineering, and Cloud Platforms. Preferred Certifications • Microsoft Certified: Azure AI Engineer Associate (AI-102) • Microsoft Certified: Azure Data Engineer Associate (DP-203) • Databricks Generative AI Certification • AWS Certified Machine Learning Specialty • Google Professional Machine Learning Engineer Soft Skills • Strong communication and presentation skills. • Excellent stakeholder management capabilities. • Ability to translate business requirements into AI solutions. • Team leadership and mentoring experience. • Strategic thinking and problem-solving skills. Key Deliverables • Enterprise AI Solutions • Generative AI Applications • Agentic AI Frameworks • RAG-Based Knowledge Systems • LLM-Powered Business Applications • AI Governance & MLOps Frameworks • Production-Ready Machine Learning Solutions This role is ideal for a seasoned AI professional capable of leading enterprise-wide AI transformation initiatives while remaining hands-on with modern Generative AI, Agentic AI, NLP, Data Engineering, and LLM technologies.