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**
- 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).

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

Work Location: In person