{"id":3215603,"url":"https://alion.io/job/microsoft-bing-staff-engineer-ai","title":"Staff Engineer - AI","company":{"id":2037,"name":"Microsoft Bing","domain":"bing.com","url":"https://alion.io/company/microsoft-bing","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":9,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"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":31000,"max_usd":72000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":9,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Confluence","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"Jira","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scrum","optional":false},{"name":"Splunk","optional":false},{"name":"SQL","optional":false},{"name":"Weaviate","optional":false},{"name":"Feast","optional":true},{"name":"Feature Store","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LLM","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-24T21:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-24T21:35:00Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Enterprise Data and Analytics thrives on strong relationships with our business and IT partners and working diligently to address their needs, which support TJX growth and operational stability. On this tightly knit and fast-paced team you will be constantly challenged to stretch and think outside the box. We are looking for someone who embraces the use of emerging technology to design, build, deploy, and govern AI solutions.\n\nResponsibilities: \nDesign, develop, test, and deploy AI solutions using Azure AI services to meet business requirements.\nTrain, fine-tune, and evaluate AI models, including large language models (LLMs), ensuring they meet performance criteria and integrate seamlessly into new or existing solutions.\nDevelop and integrate APIs to enable smooth interaction between AI models and other applications, facilitating efficient model serving.\nCollaborate effectively with cross-functional teams, including data scientists, software engineers, and business stakeholders, to deliver comprehensive AI solutions.\nOptimize AI and ML model performance through techniques such as hyperparameter tuning and model compression to enhance efficiency and effectiveness.\nMonitor and maintain AI systems, providing technical support and troubleshooting to ensure continuous operation and reliability.\nCreate comprehensive documentation for AI solutions, including design documents, user guides, and operational procedures, to support development and maintenance.\nStay updated with the latest advancements in AI, machine learning, and cloud technologies, demonstrating a commitment to continuous learning and improvement.\nDesign, code, deploy, and support software components, working collaboratively with AI architects and engineers to build impactful systems and services.\nLead medium to large initiatives, prioritizing and assigning tasks, providing guidance, and resolving issues to ensure successful project delivery.\nDesign, develop, and deploy AI/ML and GenAI solutions in a cloud-native (Azure) environment.\nBuild and operationalize end-to-end MLOps pipelines, including model training, deployment, monitoring, and versioning, using tools such as Azure ML, MLflow, Databricks, and Azure DevOps/GitHub.\nImplement RAG pipelines, prompt engineering, guardrails, and LLMOps best practices for scalable GenAI applications.\nDevelop secure, scalable APIs and integrate with Azure AI Services (Document Intelligence, Search, Vision, Speech).\nWork with vector databases (FAISS, Weaviate, Pinecone, pgvector, Azure Cognitive Search) for embeddings and retrieval workflows.\nCollaborate in an Agile/Scrum environment, including requirements gathering, design, testing, and documentation using Jira and Confluence.\nEnsure AI solutions follow security best practices such as encryption, access control, and safe deployment principles.\nApply ethical AI principles, including bias detection, responsible ML, and fairness.\nTroubleshoot issues using monitoring tools like App Insights, Azure Monitor, and Splunk.\nStay current on the latest developments in GenAI, LLMs, NLP, and multimodal AI.\n\nRequirements: \nBachelor's degree in computer science, Engineering, or a related field.\n5+ years of hands-on AI/ML development experience with production deployments.\nStrong proficiency in Python, SQL, and modern software engineering practices.\nExperience with Azure AI Foundry, Azure OpenAI, Azure AI Search, and core Azure cloud services.\nExpertise in version control (Git), CI/CD pipelines for ML, and cloud DevOps workflows.\nPractical experience with data engineering tools and scalable data processing (e. g., Databricks, PySpark).\nStrong communication, analytical thinking, and problem-solving abilities.\nKnowledge of security best practices for AI systems and responsible AI considerations.\n\nPreferred Qualifications: \nExperience with LLM application frameworks such as LangChain, Semantic Kernel, and LlamaIndex.\nExperience with multimodal AI use cases and advanced GenAI workloads.\nKnowledge of feature stores and ML operations tools such as Feast, Tecton, Evidently, or WhyLabs.\nFamiliarity with vector search implementation at scale.\nAbility to design guardrails and content safety workflows for LLM applications.\nStrong passion for continuous learning, research, 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