2 months ago
Bengaluru, IndiaStaff+
Responsibilities
- Lead the design, development, and deployment of AI and Generative AI solutions aligned with business objectives and AI strategy.
- Design end-to-end ML and AI architectures supporting scalability, real-time inference, and model governance.
- Develop, train, and validate models using deep learning, ensemble methods, transfer learning, LLMs, and agentic systems.
- Build production AI/ML pipelines covering training, evaluation, deployment, monitoring, retraining, reproducibility, and versioning.
- Implement AIOps and MLOps practices for models and LLMs.
- Implement AI guardrails, safety frameworks, compliance controls, fairness standards, explainability requirements, and business risk controls.
- Establish monitoring and governance for model performance drift, data drift, business metrics, versioning, approval, and retirement.
- Guide business units and functions in developing AI use cases and drive adoption of AI/ML solutions across the organization.
- Translate business problems into AI/ML opportunities and work with product teams to package capabilities into scalable products and services.
- Collaborate with business leaders, technical teams, compliance functions, and other stakeholders to gather feedback and communicate progress.
- Maintain technical specifications, user guides, project plans, status reports, and other AI product documentation.
- Contribute to continuous improvement of AI product development processes and MLOps frameworks.
Requirements
- 10+ years of professional experience in end-to-end AI/ML engineering, AI/ML architecture, or a related role.
- A proven track record of delivering at least three ML/AI solutions to production, preferably in enterprise or financial services environments.
- Experience deploying Generative AI solutions and LLMs through software application integrations, including OpenAI GPT models, Google Gemini, and Mistral.
- Experience with orchestration frameworks such as LangChain and LangGraph.
- Strong Python proficiency and experience designing modular, testable, maintainable ML systems using design patterns and architectural principles.
- Hands-on experience building enterprise APIs with modern high-performance frameworks such as FastAPI.
- Experience deploying AI solutions at scale on AWS, Google Cloud Platform, or Microsoft Azure; multi-cloud capability is a plus.
- Experience working with big data, low-latency inference, feature serving, online prediction systems, and streaming data processing for ML systems.
- Knowledge of LLMs, prompt engineering, fine-tuning, retrieval-augmented generation, agentic systems, and production LLM deployment considerations.
- Understanding of model lifecycle management, version control, reproducibility, approval workflows, monitoring dashboards, and retirement processes.
- Knowledge of embeddings, vector databases, semantic search, model security, adversarial attacks, differential privacy, encryption, secure model serving, and data privacy compliance.
- Knowledge of bias detection, fairness metrics, explainable AI, algorithmic transparency, and discrimination-related regulatory requirements.
- Ability to translate business problems into AI/ML opportunities, understand ROI and business metrics, and drive adoption and commercialization.
- Strong collaboration, communication, customer focus, and ability to navigate complex challenges.
Tech Stack
Categories
About Capco
Capco is a global technology and management consultancy focused on financial services, helping banks, capital markets, insurance, and wealth firms with digital transformation, risk and compliance, and data/AI-enabled modernization. Founded in 1998 and headquartered in London, it operates as a Wipro company. Its business model centers on project-based consulting, managed services, and solution delivery across strategy, engineering, and platforms for regulated institutions.