AI Solutions Engineer

About the job

About the Opportunity Job Type: Permanent

Application Deadline: 30 October 2026

Job Description

Title                 AI Solutions Engineer Department       AMP – Research & SI Location          Bengaluru or Gurgaon, India Reports To       Principal Engineer Level                4

We’re proud to have been helping our clients build better financial futures for over 50 years. How have we achieved this? By working together - and supporting each other - all over the world. So, join our team and feel like you’re part of something bigger.

Our clients come from all walks of life and so do we. We are proud of our inclusive culture and encourage applications from the widest mix of talent, whatever your age, gender, ethnicity, sexual orientation, gender identity, social background and more.

As a flexible employer, we trust our people to perform their role in the way that works best for them, our clients and our business. We are a disability-friendly company and would welcome a conversation with you if you feel you might benefit from any reasonable adjustments to perform to the best of your ability during the recruitment process and beyond.

About your team Asset Management Platforms (AMP) is responsible for the design, engineering and delivery of technology solutions that support Fidelity's global Asset Management business. We partner with Research Analysts, Portfolio Managers, Asset Management Operations, Sustainability teams, and Distribution teams across Europe and Asia to deliver modern platforms that enhance investment research, knowledge discovery, data-driven insights, and informed investment decision-making.

Our team develops and supports the technology ecosystem used across multiple investment capabilities, including Equities, Fixed Income, Multi-Asset, Systematic Investing, and Sustainable Investing. Our platforms underpin critical research processes including company and market research, sustainability analysis, financial modelling, investment content management, knowledge discovery, collaboration, and research analytics, enabling investment professionals to generate high-quality investment insights and improve client outcomes.

As Artificial Intelligence continues to transform investment research and software engineering, we are investing in modern engineering practices that combine AI-assisted software development with AI-powered research capabilities to improve productivity, accelerate insight generation, and enhance engineering quality. The AI Solutions Engineer will play a key role in designing, developing, and delivering enterprise AI solutions while enabling engineering teams to adopt AI-enabled development practices safely, effectively, and responsibly across the Research Technology platform.

About your role The AI Solutions Engineer is a hands-on technical engineer responsible for accelerating software engineering delivery through the effective adoption of Artificial Intelligence and modern engineering practices.

Working closely with Engineering Leads, Solution Architects, Product Owners and software engineering teams, you will identify opportunities to improve engineering productivity through AI-assisted development while maintaining Fidelity's high standards for architecture, engineering governance, software quality and security.

You will combine strong software engineering expertise with practical knowledge of Large Language Models (LLMs) and AI-powered development tooling to establish best practices for AI-assisted software engineering. This includes to make effective use of technologies such as Claude Code, GitHub Copilot and other AI coding assistants to improve software design, implementation, testing, documentation and operational support.

The role is equally focused on engineering excellence and innovation. You will apply modern software and AI engineering practices through hands-on design, development, testing, deployment, and production support, contribute to architectural decisions within established standards, support the adoption and continuous improvement of CI/CD, DevSecOps practices and implement approved security, governance, and Responsible AI controls throughout the software development lifecycle.

This is an exciting opportunity to shape how AI is adopted across Asset Management Platforms, helping engineering teams deliver high-quality software faster while ensuring solutions remain secure, maintainable and aligned with Fidelity's engineering standards.

Key Responsibilities AI Engineering Enablement

- Design, build, deploy, and support enterprise AI applications using LLMs, RAG, embeddings, vector search, tool calling, and agentic workflows.

- Build grounded RAG pipelines covering document ingestion, parsing, chunking, metadata enrichment, hybrid retrieval, reranking, citation, and response validation.

- Develop AI agents capable of planning, reasoning, maintaining context, invoking approved tools, and executing governed business workflows.

- Integrate commercial and open-source models through platforms such as AWS Bedrock, Azure OpenAI, OpenAI, Anthropic, or equivalent services.

- Implement prompt and model configuration management, evaluation datasets, automated quality testing, guardrails, and human-in-the-loop controls.

- Evaluate AI technologies progmatically and select solutions based on business value, quality, security, latency, scalability, and cost.

- Coach and mentor engineering teams in the effective use of Claude Code, GitHub Copilot and other AI-assisted development tools.

Software Engineering

- Build complete AI-assisted apps using modern frontend technologies such as Angular or React, TypeScript, JavaScript, HTML, and CSS.

- Develop secure and scalable APIs, microservices, and orchestration services using Python, FastAPI, Node.js, or NestJS.

- Integrate applications with enterprise APIs, market and research data, document repositories, collaboration platforms, and structured and unstructured data sources.

- Work with Relational, NoSQL, Cloud and vector technologies such as PostgreSQL, OpenSearch, Snowflake and pgvector.

- Participate in engineering practices, including modular design, API contracts, code reviews, performance optimization and secure coding.

Engineering Excellence

- Champion modern engineering practices including Continuous Integration, Continuous Delivery and DevSecOps.

- Work with engineering teams to optimise software delivery pipelines.

- Promote unit test automation and engineering best practises throughout the SDLC.

- Create reusable AI services, reference implementations, engineering standards, and ready-to-use development capabilities that improve developer productivity.

Architecture & Governance

- Collaborate with Solution Architects and Engineering Leads to ensure solutions align with Fidelity's target architecture.

- Ensure AI-assisted software development complies with engineering standards, security policies and regulatory requirements.

- Promote sound software engineering principles including maintainability, scalability, resilience and observability.

- Implement controls for prompt injection, sensitive-data exposure, unsafe responses, hallucination, unauthorized tool use, and third-party model risk.

- Balance innovation with appropriate technology risk management and governance.

- Communicate technical decisions, risks, trade-offs, and delivery progress clearly to engineering, architecture, product, and business stakeholders.

Stakeholder Engagement

- Build strong working relationships with Engineering Leads, Product Owners, Solution Architects and software engineering teams.

- Partner with investment technology stakeholders to understand engineering challenges and identify opportunities for AI-enabled improvement.

- Establish and support communities of practice around AI-assisted software engineering.

- Deliver demonstrations, technical workshops and knowledge-sharing sessions with senior investment professionals.

- Stay informed on emerging AI technologies and assess their applicability within Fidelity's engineering environment.

About you We’re looking for an experienced, hands-on AI Engineer who combines strong full-stack engineering capability with practical experience in Artificial Intelligence, Machine Learning, and Generative AI. You are comfortable taking AI-powered applications and services through design, development, integration, testing, deployment, and production support within established architecture and engineering standards.

You are naturally curious about emerging technologies, enjoy solving complex engineering challenges, and apply AI pragmatically to improve business outcomes and developer productivity without compromising software quality, security, governance, or Responsible AI principles.

You are comfortable contributing to architectural and technical discussions with senior engineers, collaborating with Product, Architecture, Data Engineering, and Quality Engineering teams, and engaging with investment stakeholders to understand their needs and translate them into secure, scalable, and production-ready AI solutions.

Software Engineering Experience

- 6+ years experience in software engineering, including experience leading the technical delivery of complex enterprise applications.

- Demonstrable experience building, deploying, and supporting production Generative AI or LLM-powered applications.

- Strong hands-on engineering skills in Python and at least one modern frontend stack using TypeScript with Angular / React.

- Experience developing APIs and distributed services using frameworks such as FastAPI, Node.js, or NestJS.

- Strong experience with RAG, including embeddings, vector search, hybrid retrieval, reranking, grounding, citations, and evaluation.

- Experience building AI agents or orchestrated AI workflows using LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, or equivalent frameworks.

- Experience with LLM capabilities such as structured output, function or tool calling, conversation state, prompt management, and model routing.

- Strong experience designing and delivering enterprise software solutions.

- Excellent understanding of modern software engineering principles and design patterns.

- Experience working within Agile software delivery teams.

- Strong understanding of engineering governance, software quality and secure software development practices.

- Experience implementing and maintaining CI/CD pipelines.

- Experience with DevSecOps practices and engineering automation.

AI Engineering Experience

- Practical experience using AI-assisted software development tools such as Claude Code, GitHub Copilot, Cursor, Windsurf or similar LLM-based engineering assistants.

- Experience integrating AI into software engineering workflows including code generation, documentation, automated testing, code review and developer productivity.

- Understanding of prompt engineering and effective techniques for interacting with Large Language Models.

- Familiarity with emerging AI concepts such as AI agents, Model Context Protocol (MCP), Retrieval Augmented Generation (RAG) and agentic development workflows.

- Understanding of AI governance, responsible AI practices and the effective use of AI within enterprise engineering environments.

- Passion for staying current with developments in AI-assisted software engineering.

Technical Skills Experience with many of the following technologies and practices:

- Python, Angular, Nodejs, TypeScript or similar modern programming languages

- LLM application development and prompt engineering

- Enterprise RAG and vector search

- AI agents, tool calling, and workflow orchestration

- Cloud platforms (AWS, Azure or GCP)

- Containerisation technologies including Docker and Kubernetes

- Git-based source control and collaborative development workflows

- REST APIs and event-driven architectures