{"id":3352271,"url":"https://alion.io/job/workday-senior-software-engineer-3","title":"Lead Software Engineer/ Senior Software Engineer- Backend & AI/ML & Cloud","company":{"id":2786132,"name":"Workday","domain":"myworkdayjobs-impl.com","url":"https://alion.io/company/myworkdayjobs-impl","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":48,"ghost_share":0,"stale_share":0.042,"repost_share":0.063,"time_to_fill_p50_days":21,"computed_at":"2026-11-03T05:54:40Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":16500,"max_usd":38000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":168},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Alembic","optional":false},{"name":"Amazon Aurora","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Anthropic","optional":false},{"name":"Apache Kafka","optional":false},{"name":"API Gateway","optional":false},{"name":"Asyncio","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS CDK","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Chain-of-Thought","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"CloudFormation","optional":false},{"name":"CrewAI","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dependency Injection","optional":false},{"name":"Docker","optional":false},{"name":"DynamoDB","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"GitOps","optional":false},{"name":"Hugging Face","optional":false},{"name":"IAM","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Mistral","optional":false},{"name":"MLFlow","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Pydantic","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQLAlchemy","optional":false},{"name":"Swagger","optional":false},{"name":"Terraform","optional":false},{"name":"Tokenization","optional":false},{"name":"Weaviate","optional":false},{"name":"Azure","optional":true},{"name":"GraphQL","optional":true},{"name":"Multimodal AI","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Pulumi","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-27T18:55:12Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-03T12:46:12Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"FactSet creates flexible, open data and software solutions for over 200,000 investment professionals worldwide, providing instant access to financial data and analytics that investors use to make crucial decisions.\nAt FactSet, our values are the foundation of everything we do. They express how we act and operate, serve as a compass in our decision-making, and play a big rolein how we treat each other, our clients, and our communities. We believe that the best ideas can come from anyone, anywhere, at any time, and that curiosity is the key to anticipatingour clients’ needs and exceeding their expectations.\nYour Team's Impact\n We are looking for a Lead/Senior Software Engineer who is passionate about Generative AI, Agentic systems, and cloud-native API development. In this role, you will be a key contributor in designing, building, and deploying production-grade AI-powered applications and intelligent agent frameworks that transform the way our products and customers interact with data and automation.\nYou will collaborate closely with ML Engineers, Data Scientists, Product Managers, and Cloud Architects to deliver scalable, reliable, and innovative solutions powered by cutting-edge GenAI technologies.\nWhat You'll Do\nGenAI & Agentic Development\nDesign and develop Agentic AI workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar\nBuild and integrate LLM-powered applications using models from OpenAI, Anthropic, Meta (LLaMA), Mistral, or foundation models on cloud platforms\nImplement Retrieval-Augmented Generation (RAG) pipelines, vector stores, and semantic search capabilities\nDevelop multi-agent orchestration systems with defined tool use, memory management, and reasoning loops\nEvaluate, fine-tune, and optimize LLM behavior through prompt engineering and chain-of-thought design \nAPI Development & Backend Engineering\nBuild high-performance, scalable RESTful APIs and async microservices using FastAPI and Python\nDesign clean, modular, and well-documented API contracts following OpenAPI/Swagger standards\nImplement authentication, authorization, rate limiting, and security best practices across APIs\nDevelop event-driven and streaming architectures to support real-time AI inference pipelines\n\nCloud & Platform Engineering\nDeploy and manage AI workloads on AWS (SageMaker, Lambda, API Gateway, Bedrock, S3, ECS/EKS)\nUtilize Databricks for large-scale data processing, MLflow model tracking, and AI/ML pipeline orchestration\nBuild CI/CD pipelines and infrastructure-as-code (IaC) using tools like Terraform, AWS CDK, or similar\nEnsure scalability, observability, and reliability through cloud-native monitoring and logging practices\n\nEngineering Excellence\nWrite clean, testable, production-ready Python code following software engineering best practices\nConduct code reviews and mentor junior engineers on GenAI patterns and best practices\nContribute to architectural decisions, technical roadmaps, and engineering documentation\nStay current with the rapidly evolving GenAI ecosystem and champion the adoption of emerging tools\nWhat We're Looking For\n\n5-12 years of professional software engineering experience\nHands-on Python development in production environments\nExperience with GenAI technologies including LLMs, RAG, embeddings, or prompt engineering\nBuilding and deploying APIs using FastAPI or similar async Python frameworks\nExperience working with AWS cloud services in a production environment\nHands-on experience with Databricks for data/AI workloads\nPython & Frameworks\nExpert-level Python (async programming, type hints, Pydantic, dependency injection)\nFastAPI - routing, middleware, background tasks, OpenAPI spec generation\nExperience with SQLAlchemy, Alembic, or async ORMs\nUnit/integration testing with pytest, pytest-asyncio\n\nGenAI & Agentic\nStrong understanding of LLM internals, tokenization, context windows, and model behavior\nExperience with LangChain / LangGraph / AutoGen / CrewAI or similar agentic frameworks\nFamiliarity with vector databases - Pinecone, Weaviate, pgvector, ChromaDB, or FAISS\nKnowledge of RAG architectures, chunking strategies, and embedding models\nHands-on experience with OpenAI API, Anthropic Claude API, AWS Bedrock, or Hugging Face models\n\nCloud - AWS\nAWS Bedrock - LLM model invocation and management\nAWS Lambda, API Gateway, ECS/EKS - serverless and containerized deployments\nS3, DynamoDB, RDS/Aurora - data storage patterns\nIAM, VPC, Secrets Manager - security and access control\nFamiliarity with AWS CDK or CloudFormation\n\nDatabricks\nExperience with Databricks Workflows, Delta Lake, Unity Catalog\nFamiliarity with MLflow for experiment tracking and model registry\nUsing Databricks for AI/BI or LLM serving pipelines\n\nGeneral Engineering\nDocker & Kubernetes - containerization and orchestration\nGit - branching strategies, PR workflows, GitOps\nCI/CD - GitHub Actions, GitLab CI, Jenkins, or similar\nUnderstanding of distributed systems, event streaming (Kafka or AWS Kinesis), and microservice patterns\n\nPreferred / Nice-to-Have Qualifications\nExperience with multi-modal AI (vision + language models)\nFamiliarity with Azure OpenAI Service or Google Vertex AI\nExposure to Terraform or Pulumi for infrastructure automation\nContributions to open-source AI/ML projects\nExperience with GraphQL APIs\nBackground in data engineering or MLOps pipelines\nAWS Certifications (Solutions Architect, ML Specialty)\nDatabricks Certified Associate or Professional badge\n\nEducation\nBachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related technical field\nEquivalent practical experience and demonstrable project portfolio will be considered.\nWhat's In It For You\nAt FactSet, our people are our greatest asset, and our culture is our biggest competitive advantage. Being a FactSetter means:\nThe opportunity to join an S&P 500 company with over 45 years of sustainable growth powered by the entrepreneurial spirit of a start-up.\nSupport for your total well-being. This includes health, life, and disability insurance, as well as retirement savings plans and a discounted employee stock purchase program, plus paid time off for holidays, family leave, and company-wide wellness days.\nFlexible work accommodations. We value work/life harmony and offer our employees a range of accommodations to help them achieve success both at work and in their personal lives.\nA global community dedicated to volunteerism and sustainability, where collaboration is always encouraged, and individuality drives solutions.\nCareer progression planning with dedicated time each month for learning and development. \nBusiness Resource Groups open to all employees that serve as a catalyst for connection, growth, and belonging. \nLearn more about our benefits here.\nCompany Overview:\nFactSet (NYSE:FDS| NASDAQ:FDS) helps the financial community to see more, think bigger, and work better. Our digital platform and enterprise solutions deliver financial data, analytics, and open technology to more than 8,200 global clients, including over 200,000 individual users. Clients across the buy-side and sell-side, as well as wealth managers, private equity firms, and corporations, achieve more every day with our comprehensive and connected content, flexible next-generation workflow solutions, and client-centric specialized support. As a member of the S&P 500, we are committed to sustainable growth and have been recognized among the Best Places to Work in 2023 by Glassdoor as a Glassdoor Employees’ Choice Award winner. Learn more at www.factset.com and follow us on X and LinkedIn.\nAt FactSet, we celebrate difference of thought, experience, and perspective. 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