AI/ML – Solution Architect

Role Overview

We are seeking an experienced AI/ML Solution Architect who can design, develop, and implement enterprise-grade AI and machine learning solutions. The role involves working closely with business stakeholders, data scientists, data engineers, and development teams to deliver scalable AI/ML models, integrate them into products/platforms, and drive adoption across the organization. The ideal candidate should have deep expertise in AI/ML technologies, cloud platforms, solution architecture, and the ability to align technical solutions with business objectives.

Key Responsibilities

Solution Design & Architecture

Define end-to-end AI/ML solution architectures, including data pipelines, model training, deployment, monitoring, and lifecycle management.

Translate business problems into AI/ML use cases and recommend appropriate algorithms, tools, and frameworks.

Ensure AI/ML solutions are scalable, secure, and aligned with enterprise architecture standards.

Technical Leadership

Guide data scientists and engineers in developing and optimizing machine learning models.

Provide best practices for feature engineering, model training, validation, and deployment.

Evaluate and recommend AI/ML platforms, tools, and frameworks.

Implementation & Delivery

Work with cross-functional teams to implement ML models into production (batch/real-time inference).

Define CI/CD pipelines for ML (MLOps) to ensure model reproducibility and governance.

Collaborate with DevOps, cloud engineers, and product teams for seamless integration.

Stakeholder Management

Partner with business units to identify opportunities where AI/ML can add value.

Present solution architecture, technical choices, and business impact to stakeholders.

Support pre-sales, RFPs, and client discussions by providing AI/ML solution expertise.

Required Skills & Qualifications

Bachelor’s/Master’s degree in Computer Science, Data Science, AI/ML, or related field.

8–12 years of IT experience with at least 4–6 years in AI/ML solution design and deployment.

Strong expertise in machine learning, deep learning, NLP, computer vision, and generative AI.

Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, Hugging Face).

Proficiency in Python, R, or Java, with strong coding and debugging skills.

Solid knowledge of MLOps practices (CI/CD pipelines, model monitoring, versioning).

Experience with cloud platforms (AWS Sagemaker, Azure ML, Google Vertex AI).

Understanding of data architecture (data lakes, ETL pipelines, APIs, data governance).

Familiarity with microservices, APIs, and integration of ML models with enterprise applications.

Strong problem-solving, communication, and leadership skills.

Preferred Skills (Good to Have)

Knowledge of LLMs (Large Language Models) and prompt engineering.

Experience in AI ethics, fairness, and responsible AI frameworks.

Exposure to edge AI or IoT-based ML deployments.

Experience in big data tools (Spark, Hadoop, Databricks).

Contributions to open-source AI/ML projects or research publications.