{"id":3297639,"url":"https://alion.io/job/fidelity-international-ai-solutions-engineer","title":"AI Solutions Engineer","company":{"id":1819505,"name":"Fidelity International","domain":"fidelity.co.uk","url":"https://alion.io/company/fidelity-co-uk","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":5,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-11-01T06:10:25Z"}},"role":"Solutions","role_family":"Solutions","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":45000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":15},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Angular","optional":false},{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"CrewAI","optional":false},{"name":"Cursor","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"JavaScript","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Nest.JS","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Snowflake","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TypeScript","optional":false},{"name":"Windsurf","optional":false}],"status":"closed","first_seen_at":"2026-10-26T22:19:47Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-31T15:25:08Z","board_verified":false,"closed_at":"2026-10-31T15:25:08Z","days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"About the Opportunity\nJob Type: PermanentApplication Deadline: 30 October 2026Job Description\nTitle AI Solutions Engineer\nDepartment AMP - Research & SI\nLocation Bengaluru or Gurgaon, India\nReports To Principal Engineer\nLevel 4\nWe’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.\nOur 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.\nAs 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.\nAbout your team\nAsset 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.\nOur 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.\nAs 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.\nAbout your role\nThe 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.\nWorking 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.\nYou 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.\nThe 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.\nThis 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.\nKey Responsibilities\nAI Engineering Enablement\nDesign, build, deploy, and support enterprise AI applications using LLMs, RAG, embeddings, vector search, tool calling, and agentic workflows.\nBuild grounded RAG pipelines covering document ingestion, parsing, chunking, metadata enrichment, hybrid retrieval, reranking, citation, and response validation.\nDevelop AI agents capable of planning, reasoning, maintaining context, invoking approved tools, and executing governed business workflows.\nIntegrate commercial and open-source models through platforms such as AWS Bedrock, Azure OpenAI, OpenAI, Anthropic, or equivalent services.\nImplement prompt and model configuration management, evaluation datasets, automated quality testing, guardrails, and human-in-the-loop controls.\nEvaluate AI technologies progmatically and select solutions based on business value, quality, security, latency, scalability, and cost.\nCoach and mentor engineering teams in the effective use of Claude Code, GitHub Copilot and other AI-assisted development tools.\nSoftware Engineering\nBuild complete AI-assisted apps using modern frontend technologies such as Angular or React, TypeScript, JavaScript, HTML, and CSS.\nDevelop secure and scalable APIs, microservices, and orchestration services using Python, FastAPI, Node.js, or NestJS.\nIntegrate applications with enterprise APIs, market and research data, document repositories, collaboration platforms, and structured and unstructured data sources.\nWork with Relational, NoSQL, Cloud and vector technologies such as PostgreSQL, OpenSearch, Snowflake and pgvector.\nParticipate in engineering practices, including modular design, API contracts, code reviews, performance optimization and secure coding.\nEngineering Excellence\nChampion modern engineering practices including Continuous Integration, Continuous Delivery and DevSecOps.\nWork with engineering teams to optimise software delivery pipelines.\nPromote unit test automation and engineering best practises throughout the SDLC.\nCreate reusable AI services, reference implementations, engineering standards, and ready-to-use development capabilities that improve developer productivity.\nArchitecture & Governance\nCollaborate with Solution Architects and Engineering Leads to ensure solutions align with Fidelity's target architecture.\nEnsure AI-assisted software development complies with engineering standards, security policies and regulatory requirements.\nPromote sound software engineering principles including maintainability, scalability, resilience and observability.\nImplement controls for prompt injection, sensitive-data exposure, unsafe responses, hallucination, unauthorized tool use, and third-party model risk.\nBalance innovation with appropriate technology risk management and governance.\nCommunicate technical decisions, risks, trade-offs, and delivery progress clearly to engineering, architecture, product, and business stakeholders.\nStakeholder Engagement\nBuild strong working relationships with Engineering Leads, Product Owners, Solution Architects and software engineering teams.\nPartner with investment technology stakeholders to understand engineering challenges and identify opportunities for AI-enabled improvement.\nEstablish and support communities of practice around AI-assisted software engineering.\nDeliver demonstrations, technical workshops and knowledge-sharing sessions with senior investment professionals.\nStay informed on emerging AI technologies and assess their applicability within Fidelity's engineering environment.\nAbout you\nWe’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.\nYou 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.\nYou 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.\nSoftware Engineering Experience\n6+ years experience in software engineering, including experience leading the technical delivery of complex enterprise applications.\nDemonstrable experience building, deploying, and supporting production Generative AI or LLM-powered applications.\nStrong hands-on engineering skills in Python and at least one modern frontend stack using TypeScript with Angular / React.\nExperience developing APIs and distributed services using frameworks such as FastAPI, Node.js, or NestJS.\nStrong experience with RAG, including embeddings, vector search, hybrid retrieval, reranking, grounding, citations, and evaluation.\nExperience building AI agents or orchestrated AI workflows using LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, or equivalent frameworks.\nExperience with LLM capabilities such as structured output, function or tool calling, conversation state, prompt management, and model routing.\nStrong experience designing and delivering enterprise software solutions.\nExcellent understanding of modern software engineering principles and design patterns.\nExperience working within Agile software delivery teams.\nStrong understanding of engineering governance, software quality and secure software development practices.\nExperience implementing and maintaining CI/CD pipelines.\nExperience with DevSecOps practices and engineering automation.\nAI Engineering Experience\nPractical experience using AI-assisted software development tools such as Claude Code, GitHub Copilot, Cursor, Windsurf or similar LLM-based engineering assistants.\nExperience integrating AI into software engineering workflows including code generation, documentation, automated testing, code review and developer productivity.\nUnderstanding of prompt engineering and effective techniques for interacting with Large Language Models.\nFamiliarity with emerging AI concepts such as AI agents, Model Context Protocol (MCP), Retrieval Augmented Generation (RAG) and agentic development workflows.\nUnderstanding of AI governance, responsible AI practices and the effective use of AI within enterprise engineering environments.\nPassion for staying current with developments in AI-assisted software engineering.\nTechnical Skills\nExperience with many of the following technologies and practices:\nPython, Angular, Nodejs, TypeScript or similar modern programming languages\nLLM application development and prompt engineering\nEnterprise RAG and vector search\nAI agents, tool calling, and workflow orchestration\nCloud platforms (AWS, Azure or GCP)\nContainerisation technologies including Docker and Kubernetes\nGit-based source control and collaborative development workflows\nREST APIs and event-driven architectures\nCI/CD tooling\nInfrastructure as Code\nObservability and monitoring platforms\nEnterprise architecture principles\nInvestment Management Experience\nExperience delivering technology solutions within Asset Management, Investment Management or Financial Services.\nStrong understanding of front-to-back investment 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