AI & Agentic Systems Engineering, AVP

Overview

Engineer, AVP supporting the design and delivery of enterprise-grade AI, generative AI, agentic systems and modern technology solutions. The role translates business and investment challenges into scalable technical architectures, production-ready AI services and reusable solutions.

What you'll do

• Assist the architecture and delivery of enterprise-grade AI solutions using Generative AI, Large Language Models and agentic frameworks, including AI copilots, domain-specific assistants and multi-agent workflows.
• Design planning, orchestration, reviewer, evaluator and execution agents with human-in-the-loop controls, safety mechanisms and monitoring.
• Translate prioritized business and investment challenges into scalable technical architectures and production-ready AI services.
• Support the development of LLM-powered applications using commercial and open-source models and platforms, applying prompt engineering, structured outputs, reasoning frameworks and autonomous workflows.
• Design retrieval-augmented generation solutions using semantic and hybrid search, embeddings, enterprise knowledge bases, metadata enrichment, reranking and retrieval-quality optimization.
• Establish LLMOps capabilities covering prompt lifecycle management, model evaluation, observability, performance and cost monitoring, testing, validation and responsible AI controls.
• Develop modern React and TypeScript front ends and Python- or Java-based back-end services, APIs and microservices.
• Build reliable data and analytics solutions using BigQuery and PostgreSQL, supported by cloud-native, event-driven and distributed architectures.
• Apply automated testing, CI/CD, infrastructure as code, containerization, observability, monitoring, logging and site reliability practices.
• Aid the design of predictive and analytical models, including classification, ranking, recommendation and forecasting solutions.
• Apply feature engineering, model explainability and robust validation to deliver transparent, decision-relevant analytics.
• Bring practical understanding of asset management, investment products and performance and risk measures to relevant solutions.
• Support technical strategy, architecture and reusable engineering patterns for AI-powered products and platforms.
• Enable the translation of prototypes from proof of concept to enterprise deployment.
• Partner with product managers, business stakeholders, use case owners and control functions to align priorities, manage trade-offs and deliver measurable outcomes.

What you'll need

• Hands-on software engineering experience, including the design and delivery of enterprise-scale distributed applications and production-grade AI or Generative AI solutions.
• Expertise in ReactJS and TypeScript.
• Strong Python or Java engineering skills and practical experience with API, microservices and asynchronous application design.
• Experience with BigQuery and PostgreSQL, cloud-native architectures, Git-based development, CI/CD, Docker, Kubernetes, infrastructure as code, observability and automated testing.
• Practical expertise in LLM applications, AI agents, RAG architectures, vector databases, semantic or hybrid search, model evaluation and LLMOps.
• Solid grounding in machine learning, feature engineering, model explainability and analytical modelling, using relevant Python ML libraries.
• Strong engineering mindset and ownership mentality, with sound judgement and the ability to structure ambiguity, solve complex problems, make pragmatic technical decisions and balance delivery speed, quality, risk, maintainability and long-term scalability.
• Excellent stakeholder management, communication and collaboration skills, with the ability to understand business needs, translate them into clear technical choices, manage expectations, influence decisions and build trusted relationships across business, product, technology and control functions.
• Ability to support delivery across the engineering lifecycle, establish fit-for-purpose standards and working practices, co-manage dependencies and trade-offs, and maintain focus on measurable business and user outcomes.
• Bachelor’s degree in Computer Science, Engineering, Science or a related discipline, or equivalent professional experience.
• Proven ability to leverage AI tools to enhance productivity and optimise workflows to solve business problems, while applying critical judgment to ensure responsible and ethical use of data and AI outputs.

Nice to have

• Experience with FastAPI, Spring Boot or comparable frameworks.
• Practical knowledge of GitHub or Azure DevOps, Terraform and OpenTelemetry.
• Strong understanding of financial markets and investment products.
• Experience in asset management, ETFs or mutual funds.
• Knowledge of key investment performance and risk metrics, including Sharpe Ratio, Information Ratio, Sortino Ratio, Alpha, Beta and Tracking Error.

Details

• Location: Pune, India.
• Corporate title: AVP.