Über
Bring a builder's mindset to Fidelity's Enterprise AI/ML Platform and help us scale the next generation of high-performance, production-grade backend systems. You will work on the core platform that connects tools, agents, data, and models—designing clean service abstractions, building resilient processing pipelines, shipping developer-friendly APIs and SDKs, and turning rapid prototypes into well-engineered, maintainable Python systems. The Team We hire exceptional, driven Python engineers first—people who take pride in clean code, fast learning, and high ownership. Deep knowledge of AI/ML is not a prerequisite; the domain knowledge and context can be learned on the job. What cannot be taught is the engineering rigour, the drive, and the instinct for simplicity that we look for. What You'll Do Build the core AI/ML services running in Kubernetes and locally in 'Playground' mode Design clean abstractions over vector databases and multistep Search/Information Retrieval pipelines Own automated real-time data ingestion for RAG: connectors, streaming pipelines, chunking/embedding strategies, parallel processing, retrieval metrics, resilience & restartability while guaranteeing ACID integrity of processed data and elimination of redundant document processing. Ship developer-friendly APIs/SDKs, CLIs, and templates that make it trivial to develop agents, tools, and information retrieval pipelines at enterprise scale. Instrument everything: distributed tracing for services & agentic/tool sessions, retrieval quality metrics, performance metrics, resource usage and failure forensics. Turn rapid prototypes into resilient systems—pragmatic designs that are simple to use, which scale in hardware efficient manner, and above all as simple as possible. Read and distill open-source frameworks, keep what's valuable, replace the bloated with lean, well engineered Python modules. Team Culture: Lead through code, productivity and knowledge sharing. Ask sharp questions, challenge complexity, and encourage others to do the same. We embrace a flat hierarchy where the best ideas win, regardless of seniority. What You Bring Engineering perspective: 7+ years of professional software engineering experience, with the majority spent building and operating production-grade Python backend systems. Strong Python service engineering: sound OOP, clear interfaces, thorough tests, and an obsession with readability and maintainability. Real-world performance tuning across services and data stores: concurrency, async I/O, queues, caching, SQL/NoSQL indexing, pagination, and backpressure. Experience building event-driven systems and/or real-time pipelines for ingestion and inference. Mastery of debugging complex, distributed behavior—reproducible experiments, simulations, and evidence-driven conclusions. Comfort reading open-source code and producing simplified alternatives to minimize code legacy and cognitive load. Effective use of developer-assist tools to amplify output while keeping quality high and code bloat at minimum. Produce services metrics that help us understand parallelism services can support in stable fashion, ensuring efficient hardware utilization. Propose scaling approaches based on application hardware utilization footprint & metrics. Familiarity with key Data Science, Machine Learning, or AI libraries is a bonus, but not mandatory, as long as the candidate can demonstrate the ability to quickly learn new concepts and paradigms. Product and Ownership perspective: Fast learning across new domains, with a knack for spotting and reducing unnecessary complexity. Team works on new products, understanding and implementing latest tech is paramount, learning fast. Product sensibility: start from a blank slate, ask the right questions, and design primitives that feel 'Apple-like' in usability. Produce functional picture & design of a product, based on that write requirements, epics and come up with stories which cover entire scope. Aim is that most of the risks are identified at start, not during implementation. Collaborative communication, healthy debate, and leading by example. Comfortable switching hats to do what the projects need. Ability to work in a highly dynamic environment Attention to detail, being thorough in tests and questioning assumptions, being productive without a need for supervision. Our team takes pride in quality of our products. Relevant & Nice to have skills: DevOps practices (CI/CD, Docker, Kubernetes) and infrastructure as code. AWS skills: EC2, S3, RDS, Lambda, IAM etc. Understanding Data, performant ETL, Analytics. Why This Role Shape the core platform for AI/ML services and retrieval used across Fidelity. High ownership, fast iteration, and the chance to influence design. Work on hard, high-impact problems with a team that values simplicity, experimentation, pace and teaching. Is This Role For You? If reading this job spec brought a smile to your face or sparked a jolt of enthusiasm, you should apply (assuming adequate skill/capability levels). To enjoy and thrive in our team, you should be a highly motivated & productive individual who genuinely loves what they do. The base salary range for this position is $107,000-216,000 USD per year. Placement in the range will vary based on job responsibilities and scope, geographic location, candidate's relevant experience, and other factors. Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation. We offer a wide range of benefits to meet your evolving needs and help you live your best life at work and at home. These benefits include comprehensive health care coverage and emotional well-being support, market-leading retirement, generous paid time off and parental leave, charitable giving employee match program, and educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career. Note, the application window closes when the position is filled or unposted.
Sprachkenntnisse
- English
Hinweis für Nutzer
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