Job Description
<p><p><b>About the Opportunity :<br/></b><br/>We are looking for an accomplished Senior Solution Architect GenAI / AI / ML to lead the architecture, design, and deployment of enterprise-grade artificial intelligence and machine learning solutions.<br/><br/>The ideal candidate will possess deep expertise in Generative AI (LLMs, GPT, BERT), Natural Language Processing (NLP), and Deep Learning, combined with strong proficiency in cloud-native AI platforms, data engineering, and MLOps frameworks.<br/><br/>This is a senior strategic role that blends architectural leadership with hands-on technical innovation enabling the design of scalable AI systems that integrate data pipelines, governance frameworks, and cloud-native deployment models.<br/><br/>The architect will collaborate with CXOs, data science teams, and engineering leaders to translate business goals into intelligent, production-ready AI ecosystems.<br/><br/><b>What Youll Do :</b><br/><br/>- Architect and design large-scale AI/ML and GenAI solutions, leveraging modern LLM architectures (GPT, BERT, Llama, Claude, etc.) for enterprise use cases.<br/><br/>- Lead end-to-end solution development encompassing data ingestion, model training, fine-tuning, orchestration, and deployment.<br/><br/>- Define architecture blueprints integrating data lakes, Spark-based data pipelines, and scalable ML infrastructure on Azure, AWS, or GCP.<br/><br/>- Drive MLOps strategy, automating workflows for model versioning, CI/CD integration, retraining, and monitoring.<br/><br/>- Design NLP-driven pipelines for entity extraction, summarization, text generation, and conversational AI.<br/><br/>- Ensure architectural compliance with AI governance, security, and ethical AI frameworks, including model transparency and data lineage.<br/><br/>- Collaborate with data scientists and ML engineers to select optimal models, frameworks, and deployment strategies.<br/><br/>- Define best practices for prompt engineering, fine-tuning LLMs, and leveraging GenAI APIs for domain-specific solutions.<br/><br/>- Guide cross-functional teams on data quality, feature engineering, and performance optimization.<br/><br/>- Conduct architecture reviews, POCs, and technical deep-dives to validate AI/ML design decisions and scalability.<br/><br/>- Advise leadership on AI roadmap, emerging technologies, and strategic platform choices.<br/><br/><b>What You Bring :</b><br/><br/>- 11+ years of experience in AI/ML solution architecture, data engineering, and advanced analytics.<br/><br/>- Proven experience in designing and implementing GenAI solutions using LLMs such as GPT, BERT, T5, or custom transformer models.<br/><br/>- Strong understanding of Deep Learning frameworks (TensorFlow, PyTorch, Hugging Face) and distributed training workflows.<br/><br/>- Expertise in MLOps, CI/CD pipelines, model registries, and automation tools such as MLflow, Kubeflow, or Vertex AI.<br/><br/>- Proficiency in Python, SQL, and experience with Spark, Databricks, or Ray for large-scale data processing.<br/><br/>- Deep understanding of cloud AI ecosystems across Azure Machine Learning, AWS Sagemaker, and GCP Vertex AI.<br/><br/>- Knowledge of data lake architectures, Delta Lake, and real-time streaming pipelines.<br/><br/>- Strong background in AI governance, compliance, and responsible AI implementation.<br/><br/>- Ability to lead architecture discussions, influence technical decisions, and align AI design with enterprise objectives.<br/><br/>- Excellent communication, presentation, and stakeholder management skills, with experience engaging executive leadership.<br/><br/>- Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or related technical discipline.<br/><br/><b>Preferred Skills :</b><br/><br/>- Experience in multi-LLM orchestration, RAG (Retrieval-Augmented Generation), and vector database integrations (Pinecone, FAISS, Weaviate).<br/><br/>- Familiarity with prompt engineering and custom fine-tuning of transformer models for domain-specific needs.<br/><br/>- Exposure to AI observability, model drift detection, and bias mitigation frameworks.<br/><br/>- Certifications in cloud AI services (Azure AI Engineer, AWS Machine Learning Specialty, or GCP Professional ML Engineer)</p><br/></p> (ref:hirist.tech)