ML Technical Lead

Azilen Technologies; Ahmedabad District; On-site; INR 400,000 - 700,000; Full time

Job summary

Azilen Technologies seeks a seasoned Lead ML Architect to design, deploy and scale Generative AI and ML solutions for enterprise clients. You will mentor engineers, own end-to-end AI systems, and collaborate with product and data teams.

You will drive architecture decisions, build pipelines, and ensure production readiness, security, and observability while staying ahead of advances in MLOps and LLMs.

Qualifications

• 8+ years of software development experience.
• 4+ years deploying ML/AI solutions in production.
• 2+ years leading technical teams.
• Experience with client engagements in services/consulting.
• Strong analytical and problem-solving skills.

Responsibilities

• Lead architecture, design, and implementation of scalable ML/GenAI solutions.
• Build, deploy, and maintain production-grade ML models and AI applications.
• Design end-to-end ML pipelines from data ingestion to deployment and monitoring.
• Develop APIs and backend services to integrate AI capabilities into enterprise apps.
• Collaborate with Product Managers, Data Scientists, Engineers, and stakeholders to translate requirements into technical solutions.
• Mentor engineering teams through code reviews and best practices.
• Drive architectural decisions and technology selection across AI initiatives.
• Ensure AI systems are secure, reliable, observable, and production-ready.
• Stay current with ML, MLOps, LLMs, and Generative AI advancements.

Skills

Python

OOP

SQL

REST API

FastAPI/Flask

PyTorch

TensorFlow

Scikit-learn

XGBoost

MLflow

Kubeflow

Airflow

dbt

LangChain

LlamaIndex

OpenAI

Anthropic

HuggingFace

Pinecone

Qdrant

Chroma

Milvus

SageMaker

Azure ML

Vertex AI

Docker

Kubernetes

Cloud-native

MLOps

Monitoring

Tools

FastAPI/Flask

Job description

Lead the design, development, and deployment of scalable Machine Learning and Generative AI solutions that drive business impact. Architect production-ready AI systems, mentor engineering teams, and collaborate across functions to deliver intelligent, enterprise-grade products.

Who You Are:

• 8+ years of overall software development experience, including 4+ years of hands‑on experience building and deploying Machine Learning/AI solutions in production.
• 2 years of experience leading technical teams, driving architecture, mentoring engineers, and delivering complex AI projects.
• Experience working in software services, consulting, or product engineering environments with exposure to multiple client engagements.
• possess strong analytical and problem-solving skills with the ability to translate business challenges into scalable AI solutions.
• passionate about emerging AI technologies and continuously strive to improve engineering practices and product quality.

What Will Excite Us:

• Strong expertise in Python, Object-Oriented Programming, SQL, and REST API development using FastAPI or Flask.
• Hands‑on experience with Machine Learning frameworks such as PyTorch, TensorFlow, Scikit-learn, or XGBoost.
• Experience building and managing production ML pipelines using tools like MLflow, Kubeflow, Airflow, or dbt.
• Practical experience with Generative AI technologies, including LangChain, LlamaIndex, OpenAI, Anthropic, Hugging Face, and Vector Databases such as Pinecone, Qdrant, Chroma, or Milvus.
• Experience deploying ML workloads on AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
• Hands‑on experience with Docker, Kubernetes, and cloud‑native application deployment.
• Strong understanding of model deployment, monitoring, scalability, and MLOps best practices.
• Excellent communication skills with the ability to collaborate effectively across Product, Engineering, Data Science, and client teams.

What You'll Do:

• Lead the architecture, design, and implementation of scalable Machine Learning and Generative AI solutions.
• Build, deploy, and maintain production-grade ML models and AI applications with high availability and scalability.
• Design end-to-end ML pipelines covering data ingestion, model training, evaluation, deployment, monitoring, and continuous improvement.
• Develop APIs and backend services to integrate AI capabilities into enterprise applications.
• Collaborate with Product Managers, Data Scientists, Engineers, and stakeholders to translate business requirements into technical solutions.
• Mentor engineering teams through code reviews, technical guidance, and best engineering practices.
• Drive architectural decisions, technology selection, and technical excellence across AI initiatives.
• Ensure AI systems are secure, reliable, observable, and optimized for production workloads.
• Stay current with advancements in Machine Learning, MLOps, Large Language Models, and Generative AI to continuously improve solutions and engineering standards.