AI Architect

About Us

“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery.

WHY JOIN CAPCO?

You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.

MAKE AN IMPACT

Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.

#BEYOURSELFATWORK

Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.

CAREER ADVANCEMENT

With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.

DIVERSITY & INCLUSION

We believe that diversity of people and perspective gives us a competitive advantage.

Job Location: Pune

AI Architect (PC Level)

Role Summary

We are seeking a hands-on and detail-oriented Lead AI Architect for our team. In this role, you will be responsible for designing, developing, deploying and maintaining robust Machine Learning and Generative AI solutions. You will partner with value streams, businesses, and functions to deliver scalable, production-grade AI systems that drive business value. The ideal candidate will have a strong engineering mindset, excellent communication skills, and a passion for driving innovation through AI technology.

Leading the design, development, and deployment of AI/GenAI solutions aligned to business objectives. Driving AI strategy, architecture, and delivery across use cases and programs

Key Responsibilities

AI/ML Solution Architecture:

- Design end-to-end machine learning and AI systems that solve group-wide strategic problems; ensure architecture support scalability, real-time inference, and model governance

Model Development & Experimentation:

- Develop, train, and validate machine learning models using advanced techniques (deep learning, ensemble methods, transfer learning, LLMs, agentic systems); conduct rigorous A/B testing and statistical validation

Production AI/ML Systems:

- Build production-grade AI-ML pipelines including model training, evaluation, deployment, monitoring, and retraining workflows; implement AIOps, MLOps for models & LLMs; ensure model reproducibility and versioning

AI Guardrails & Safety:

- Implement guardrails, safety frameworks, and compliance controls for AI/ML systems; ensure models meet regulatory requirements, fairness standards, explainability requirements, and business risk tolerances

Responsible AI adoption:

- Collaborate with stakeholders to ensure responsible AI practices are adopted at scale, with a clear definition of metrics and benchmark for the same

Support for AI Use Cases:

- Provide guidance and support to value streams, businesses, and functions in the development of their own AI use cases, ensuring alignment with the Group-wide AI Strategy.

- Drive adoption of AI/ML solutions across business units; translate business problems into AI/ML opportunities; work with product teams to package AI/ML capabilities into scalable products and services

Model Monitoring & Governance:

- Implement monitoring frameworks for model performance drift, data drift, and business metrics; establish governance processes for model versioning, approval, and retirement

Stakeholder Engagement:

- Build and maintain strong relationships with stakeholders across the organization, including business leaders, technical teams, and compliance functions.

- Communicate effectively with stakeholders to gather feedback, provide updates, and address any inquiries related to AI products and use cases.

Documentation and Reporting:

- Prepare and maintain comprehensive documentation for AI products, including user guides, technical specifications, and project plans.

- Develop regular reports on the status of AI product development and deployment, highlighting key milestones, challenges, and opportunities.

Continuous Improvement:

- Stay informed about industry trends and best practices in AI product development and management to enhance the effectiveness of OUR’s AI initiatives.

- Implement/assist cross-functional teams to develop MLOps framework and techniques

- Contribute to the continuous improvement of processes and methodologies related to AI product development.

Minimum Experience and Key Competencies:

10+ years of professional experience in end-end AI/ML engineering development, AI/ML architecture design, or a related role

Proven track record delivering 3+ ML/AI solutions to production preferably in enterprise or financial services environments

Experience in deploying Gen AI solutions/LLMs through integration with software applications – Open AI GPT models, Google Gemini, Mistral, etc. and orchestration frameworks like LangChain, LangGraph

Strong Python proficiency; clean code practices; expertise in designing modular, testable, maintainable ML systems; experience with design patterns and architectural principles. Hands-on experience in building new APIs in enterprise setting using modern, high-performance frameworks like FastAPI

Hands-on expertise in building & deploying solutions on the public cloud platforms AWS/GCP/Azure etc.; multi-cloud capability a plus.

Proven experience in deploying AI solutions at scale, in production, with experience working with big data

Ability to translate business problems into AI-ML opportunities; understand ROI, business metrics, and stakeholder requirements; drive adoption and commercialization.

Functional Knowledge

• Hands-on experience with large language models, prompt engineering, fine-tuning, retrieval-augmented generation (RAG), agentic systems, and considerations for production LLM deployments
• Understanding of model lifecycle management, version control, reproducibility, approval workflows, monitoring dashboards, and retirement processes
• Experience with low-latency inference, feature serving, online prediction systems, and streaming data processing for ML systems
• Understanding of embeddings, vector databases, semantic search, applications in RAG and Agentic AI systems
• Knowledge of model security, adversarial attacks, differential privacy, encryption, secure model serving, and compliance with data privacy regulations
• Knowledge of bias detection methods, fairness metrics, explainable AI techniques, and regulatory requirements around algorithmic transparency and discrimination

Others

• Highly competent, collaborative, and outcome-oriented, with a strong ability to drive results and navigate complex challenges.
• Tenacious and committed to delivering necessary outcomes in alignment with Our mission and purpose.

A strong focus on customers and teammate needs, with a dedication to serving and supporting them in achieving their goals.

If you are keen to join us, you will be part of an organization that values your contributions, recognizes your potential, and provides ample opportunities for growth. For more information, visit . Follow us on Twitter, Facebook, LinkedIn, and YouTube.