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).