Technical Architect - ML

Overview We are seeking a seasoned **Technical Architect specializing in Machine Learning and Artificial Intelligence** to lead the design, architecture, and implementation of large-scale, production-grade AI/ML systems. This role combines deep technical expertise with strategic vision to build scalable, reliable, and ethical AI solutions that drive business impact. Responsibilities Roles & Responsibilities - Define and own the end-to-end technical architecture for AI/ML platforms and products (from data ingestion to model serving and monitoring). - Lead the design of scalable ML pipelines, MLOps frameworks, and generative AI / LLM-based systems. - Architect cloud-native AI solutions (AWS SageMaker, GCP Vertex AI, Azure ML, or multi-cloud setups). - Evaluate and select appropriate algorithms, frameworks, and tools (e.g., PyTorch, TensorFlow, JAX, LangChain, LlamaIndex, Ray, Kubeflow, MLflow, etc.). - Design systems for large-scale model training/inference (distributed training, model parallelism, quantization, efficient serving with Triton, vLLM, TGI, etc.). - Establish best practices for Responsible AI – fairness, explainability (SHAP, LIME), bias mitigation, privacy (federated learning, differential privacy), and security. - Build and govern enterprise MLOps platforms including feature stores, model registries, CI/CD for ML, experiment tracking, and observability. - Collaborate with data engineers, ML engineers, software engineers, and product teams to translate business requirements into robust technical solutions. - Drive proof-of-concepts (PoCs) and spike solutions for emerging technologies (LLMs, multimodal models, agentic systems, retrieval-augmented generation, etc.). - Mentor senior ML engineers and architects; set technical standards and conduct architecture reviews. - Stay ahead of the latest research and productionize cutting-edge techniques when they add clear business value. Requirements Technical Expertise - 12+ years of software engineering experience with at least 6+ years focused on designing and deploying production ML/AI systems at scale. - Expert-level proficiency in Python and ML frameworks (PyTorch / TensorFlow / JAX). - Hands-on experience building and deploying Large Language Models (fine-tuning, instruction tuning, RLHF, quantization, LoRA/QLoRA, inference optimization). - Deep knowledge of MLOps tools and platforms (Kubeflow, MLflow, Airflow, Dagster, Flyte, Metaflow, ZenML, etc.). - Strong understanding of distributed systems, microservices, containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform, Pulumi). - Experience with vector databases (Pinecone, Weaviate, Milvus, Qdrant) and RAG architectures. - Proven track record of designing feature stores (Feast, Tecton), online/offline inference systems, and model monitoring solutions. - Expertise in cloud platforms (AWS, GCP, Azure) and their managed ML services. Leadership & Soft Skills - Demonstrated ability to lead cross-functional technical teams and influence architecture decisions at the executive level. - Excellent communication skills – capable of explaining complex ML concepts to non-technical stakeholders. - Experience defining AI roadmaps and presenting to C-level executives. Preferred (Nice-to-Have) - Contributions to open-source ML projects. - Knowledge of enterprise data platforms (Snowflake, Databricks, BigQuery).

Roles & Responsibilities - Define and own the end-to-end technical architecture for AI/ML platforms and products (from data ingestion to model serving and monitoring). - Lead the design of scalable ML pipelines, MLOps frameworks, and generative AI / LLM-based systems. - Architect cloud-native AI solutions (AWS SageMaker, GCP Vertex AI, Azure ML, or multi-cloud setups). - Evaluate and select appropriate algorithms, frameworks, and tools (e.g., PyTorch, TensorFlow, JAX, LangChain, LlamaIndex, Ray, Kubeflow, MLflow, etc.). - Design systems for large-scale model training/inference (distributed training, model parallelism, quantization, efficient serving with Triton, vLLM, TGI, etc.). - Establish best practices for Responsible AI – fairness, explainability (SHAP, LIME), bias mitigation, privacy (federated learning, differential privacy), and security. - Build and govern enterprise MLOps platforms including feature stores, model registries, CI/CD for ML, experiment tracking, and observability. - Collaborate with data engineers, ML engineers, software engineers, and product teams to translate business requirements into robust technical solutions. - Drive proof-of-concepts (PoCs) and spike solutions for emerging technologies (LLMs, multimodal models, agentic systems, retrieval-augmented generation, etc.). - Mentor senior ML engineers and architects; set technical standards and conduct architecture reviews. - Stay ahead of the latest research and productionize cutting-edge techniques when they add clear business value.

Technical Expertise - 12+ years of software engineering experience with at least 6+ years focused on designing and deploying production ML/AI systems at scale. - Expert-level proficiency in Python and ML frameworks (PyTorch / TensorFlow / JAX). - Hands-on experience building and deploying Large Language Models (fine-tuning, instruction tuning, RLHF, quantization, LoRA/QLoRA, inference optimization). - Deep knowledge of MLOps tools and platforms (Kubeflow, MLflow, Airflow, Dagster, Flyte, Metaflow, ZenML, etc.). - Strong understanding of distributed systems, microservices, containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform, Pulumi). - Experience with vector databases (Pinecone, Weaviate, Milvus, Qdrant) and RAG architectures. - Proven track record of designing feature stores (Feast, Tecton), online/offline inference systems, and model monitoring solutions. - Expertise in cloud platforms (AWS, GCP, Azure) and their managed ML services. Leadership & Soft Skills - Demonstrated ability to lead cross-functional technical teams and influence architecture decisions at the executive level. - Excellent communication skills – capable of explaining complex ML concepts to non-technical stakeholders. - Experience defining AI roadmaps and presenting to C-level executives. Preferred (Nice-to-Have) - Contributions to open-source ML projects. - Knowledge of enterprise data platforms (Snowflake, Databricks, BigQuery).