Technical Architect - ML

Prodapt; Chennai District; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Prodapt in Chennai, India seeks a seasoned Technical Architect for ML/AI to lead design and deployment of large-scale AI systems. You will own end-to-end architecture from data ingestion to model serving, collaborating with data, software, and product teams.

Responsibilities include building scalable ML pipelines and MLOps, evaluating tools, directing multi-cloud deployments, and establishing Responsible AI practices.

Qualifications

• 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.

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.

Skills

Python

PyTorch

TensorFlow

JAX

MLOps

Kubernetes

AWS

GCP

Azure

Education

Bachelor's degree in Computer Science or related field

Tools

Kubeflow

MLflow

Airflow

Dagster

Triton

Job 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).