AI Solutions Engineer

Overview

Hands-on AI Solutions Engineer responsible for accelerating software engineering delivery through the effective adoption of Artificial Intelligence and modern engineering practices within Asset Management Platforms.

What you'll do

• 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 programmatically 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.
• 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.
• Champion modern engineering practices including Continuous Integration, Continuous Delivery and DevSecOps, and work with engineering teams to optimise software delivery pipelines.
• Promote unit test automation and engineering best practices throughout the SDLC.
• Create reusable AI services, reference implementations, engineering standards, and ready-to-use development capabilities that improve developer productivity.
• Collaborate with Solution Architects and Engineering Leads to ensure solutions align with Fidelity's target architecture and comply 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.
• Communicate technical decisions, risks, trade-offs, and delivery progress clearly to engineering, architecture, product, and business stakeholders.
• Establish and support communities of practice around AI-assisted software engineering and 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.

What you'll need

• 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.
• 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.
• Experience with many of the following technologies and practices: Python, Angular, Node.js, 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 including Docker and Kubernetes; Git-based source control; REST APIs and event-driven architectures; CI/CD tooling; Infrastructure as Code; observability and monitoring platforms; enterprise architecture principles.
• Experience delivering technology solutions within Asset Management, Investment Management or Financial Services.
• Strong understanding of front-to-back investment management processes.
• Familiarity with technology supporting portfolio management, research, trading, investment risk, investment analytics or asset management operations.
• Experience working closely with Research Analysts, Portfolio Managers, Investment Directors, Quantitative Researchers or front-office technology teams.
• Understanding of the governance, control and regulatory expectations associated with investment management technology.

Details

• Permanent position in the AMP – Research & SI department, reporting to Principal Engineer, Level 4.
• Location: Bengaluru or Gurgaon, India.
• Application deadline: 22 November 2026.