AI/ML Solution Architect

Infosys; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Infosys Bengaluru is seeking an experienced AI/ML professional to lead end-to-end engagements from discovery to rollout. You will translate business needs into scalable ML solutions and guide cross‑functional teams, including data scientists and engineers.

The role emphasizes production‑grade modeling, NLP pipeline design, and ongoing model monitoring in a dynamic client‑facing environment.

Qualifications

• Bachelor’s degree in Engineering/Technology/Computer Science or related field (e.g., BE/BTech/BSc).
• 5–9 years of overall experience with strong, hands‑on expertise in AI/ML solution development and delivery.
• Proven experience applying machine learning techniques to real‑world datasets, including model evaluation and iteration.
• Practical experience with NLP and data learning workflows, including preparing and analyzing text and structured data.
• Ability to lead technical discussions, translate requirements into approaches, and guide teams toward outcomes.

Responsibilities

• Lead end-to‑end AI/ML engagements: discovery, solution design, development, validation, and rollout.
• Partner with business and technical stakeholders to define use cases, success metrics, and implementation roadmaps.
• Design and build machine learning models aligned to business objectives, ensuring robust evaluation and performance tracking.
• Drive NLP solution development (e.g., text classification, entity extraction, search/relevance) based on problem needs.
• Establish best practices for data preparation, feature engineering, model selection, and experimentation workflows.
• Review model results and communicate insights, trade‑offs, and recommendations to both technical and non‑technical audiences.
• Collaborate with engineering teams to support model operationalization, monitoring, and continuous improvement.
• Mentor junior team members, conduct technical reviews, and contribute to reusable assets and accelerators.

Skills

AI/ML design

Solution architecture

Team leadership

Problem solving

Stakeholder management

Education

BE/BTech/BSc

MTech/MCA/MSc

Tools

Python

TensorFlow

PyTorch

NLP libraries

Job description

AI/ML Primary skills

Technology->AI-AI Engineering->AI/ML Solution Architecture and Design

Minimum Qualifications

• Bachelor’s degree (or equivalent) in Engineering/Technology/Computer Science or related field (e.g., BE/BTech/BSc).
• 5–9 years of overall experience with strong, hands‑on expertise in AI/ML solution development and delivery.
• Proven experience applying machine learning techniques to real‑world datasets, including model evaluation and iteration.
• Practical experience with NLP and data learning workflows, including preparing and analyzing text and structured data.
• Ability to lead technical discussions, translate requirements into approaches, and guide teams toward outcomes.

Preferred Qualifications

• Master’s degree (or equivalent) such as MTech/MCA/MSc in a relevant discipline.
• Experience leading consulting‑style engagements, including stakeholder management, estimation, and delivery governance.
• Strong track record of deploying AI/ML solutions into production environments with monitoring and iteration practices.
• Experience designing NLP pipelines for domain‑specific problems and improving performance through experimentation.
• Ability to define reusable frameworks, accelerators, and standards that improve team productivity and quality.

Good to have skills

MLOps, Model Monitoring, Feature Engineering, Data Visualization, Cloud Platforms

Key Responsibilities

• Lead end-to‑end AI/ML engagements: discovery, solution design, development, validation, and rollout.
• Partner with business and technical stakeholders to define use cases, success metrics, and implementation roadmaps.
• Design and build machine learning models aligned to business objectives, ensuring robust evaluation and performance tracking.
• Drive NLP solution development (e.g., text classification, entity extraction, search/relevance) based on problem needs.
• Establish best practices for data preparation, feature engineering, model selection, and experimentation workflows.
• Review model results and communicate insights, trade‑offs, and recommendations to both technical and non‑technical audiences.
• Collaborate with engineering teams to support model operationalization, monitoring, and continuous improvement.
• Mentor junior team members, conduct technical reviews, and contribute to reusable assets and accelerators.