Technical Architect - ML at PRODAPT UK LIMITED

Chennai, tamil nadu, India -

Full Time

Start Date

Immediate

Expiry Date

30 Jun, 26

Salary

0.0

Posted On

01 Apr, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Artificial Intelligence, Python, MLOps, Cloud Computing, Distributed Systems, Microservices, Containerization, Orchestration, Infrastructure-as-Code, Feature Stores, Model Monitoring, Large Language Models, Data Engineering, Software Engineering, Technical Architecture

Industry

technology;Information and Internet

Description

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).

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Responsibilities

The Technical Architect will define and own the technical architecture for AI/ML platforms, leading the design of scalable ML pipelines and cloud-native AI solutions. They will collaborate with cross-functional teams to translate business requirements into robust technical solutions.