AI Architect

About the Role

The AI Architect is a critical leadership role responsible for designing, building, and deploying scalable, reliable, and ethical Artificial Intelligence and Machine Learning (AI/ML) systems that align with our strategic business goals. This role requires a blend of deep technical expertise in ML/AI, software engineering, and system architecture to transform high-level business problems into robust, production-ready AI solutions.

You will be the bridge between data scientists, data engineers, software development teams, and business stakeholders, guiding the full lifecycle of AI systems from ideation and prototyping to deployment and MLOps.

Key Responsibilities

Architecture and Design

• Design and Architect end-to-end Machine Learning (ML) and AI pipelines, including data ingestion, feature stores, model training, serving, and monitoring.
• Define the technical architecture for AI solutions, selecting appropriate cloud platforms (AWS, Azure, GCP), ML frameworks (TensorFlow, PyTorch), and deployment methodologies (Docker, Kubernetes).
• Ensure that all AI/ML systems are scalable, secure, cost-efficient, and maintainable in a production environment.
• Develop MLOps strategies and infrastructure for continuous integration, continuous delivery (CI/CD), and automated deployment of ML models.

Strategy and Collaboration

• Collaborate with business stakeholders and data scientists to translate business objectives into clear, actionable AI/ML use cases and technical requirements.
• Establish coding standards, model governance, and ethical AI practices to ensure responsible and compliant development and deployment.
• Provide technical leadership and mentorship to data science and engineering teams on architecture best practices and emerging AI technologies.
• Stay up-to-date with the latest advancements in AI, deep learning, and generative models to recommend innovative solutions.

Implementation and Oversight

• Oversee the integration of AI models into existing business processes and applications.
• Conduct architectural reviews and performance audits of existing and proposed AI systems, recommending optimizations for latency, throughput, and resource utilization.
• Work with Data Engineering to ensure data quality and accessibility for model training and inference.

Required Qualifications

Education & Experience

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.
• [7]+ years of progressive experience in software engineering, data science, or a related field, with at least [3]+ years specifically focused on designing and deploying large-scale AI/ML systems.

Technical Skills

• Deep expertise in Machine Learning methodologies, including model development, evaluation, and tuning, with a focus on production readiness.
• Proficiency in one or more major programming languages (e.g., Python, Java, Scala) and SQL.
• In-depth experience with leading ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Demonstrated experience with Cloud AI platforms (e.g., AWS SageMaker, Azure ML, GCP Vertex AI) and associated cloud services.
• Strong knowledge of DevOps/MLOps tools and practices (Docker, Kubernetes, Git, CI/CD pipelines).
• Expert-level understanding of data architecture principles, data warehousing, data lakes, and real-time data streaming technologies (e.g., Kafka).

Soft Skills

• Excellent communication and interpersonal skills, with the ability to articulate complex technical concepts to non-technical audiences.
• Proven ability to take ownership of complex projects and lead cross-functional teams to successful delivery.
• Strong problem-solving and analytical skills with a pragmatic, data-driven approach.

Preferred Qualifications (Bonus)

• Experience with specific AI domains such as Natural Language Processing (NLP), Computer Vision, or Generative AI/Large Language Models (LLMs).
• Experience with distributed computing frameworks (Apache Spark, Dask).
• Knowledge of Data Governance, Security, and Privacy standards related to AI systems.
• Relevant professional certifications (e.g., AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure AI Engineer Associate).

Job Type: Full-time

Pay: ₹800,000.00 - ₹1,800,000.00 per year

Work Location: In person