{"id":3328372,"url":"https://alion.io/job/iqvia-senior-python-developer","title":"Senior Python Developer","company":{"id":4918,"name":"IQVIA","domain":"iqvia.com","url":"https://alion.io/company/iqvia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":78,"open_postings":525,"ghost_share":0.01,"stale_share":0.832,"repost_share":0.019,"time_to_fill_p50_days":23,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Backend","role_family":"Backend","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":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":168},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Asyncio","optional":false},{"name":"AutoGen","optional":false},{"name":"CrewAI","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"FastAPI","optional":false},{"name":"Hugging Face","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NumPy","optional":false},{"name":"OpenAI","optional":false},{"name":"Pandas","optional":false},{"name":"Pydantic","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Chroma","optional":true},{"name":"CI/CD","optional":true},{"name":"Git","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Milvus","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Pinecone","optional":true},{"name":"Qdrant","optional":true},{"name":"RAG","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true}],"status":"closed","first_seen_at":"2026-10-26T00:00:00Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-31T02:27:49Z","board_verified":false,"closed_at":"2026-10-31T02:27:49Z","days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"Core & Required Skills\n Advanced Python Ecosystem: Expert-level mastery of Python is mandatory. You must have deep experience writing clean, optimized, and asynchronous Python code, alongside proficiency in core libraries (e.g., Fastapi, Pydantic, NumPy, Pandas, Asyncio).\nSnowflake and Cortex : Building Pipelines, Multi cluster architecture, Secure Data Sharing, Search Optimisation, Tuning Query Performance by using Query Profile. Reduce Credits Consumption, Cortex Search, Cortex Analyst, Cortex AI Functions.\nAI Agentic Frameworks: Proven experience designing, building, and deploying autonomous AI agents and multi-agent systems using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or LlamaIndex. \n Model Integration & Orchestration: Direct experience working with Large Language Models (LLMs) via APIs (OpenAI, Anthropic) and open-source models (Llama, Mistral) utilizing Hugging Face. \nDatabricks & Data Engineering: Strong hands-on experience with Databricks, including building and optimising data pipelines using PySpark, working with Delta Lake, and integrating with cloud data platforms for large-scale data processing and AI/ML workloads.\nHighly Desirable Skills (Added Advantages)\n Model Context Protocol (MCP): Strong understanding or hands-on experience implementing MCP to seamlessly connect LLMs to secure, localized data sources, enterprise tools, and developmental environments.\nModern Cloud Data Warehousing: Proficiency with Snowflake database management, including writing optimized SQL, configuring Snowpark for Python, and building secure data shares for AI consumption.\n Vector Databases & RAG: Practical experience with vector databases (e.g., Pinecone, Milvus, Qdrant, Chroma) to build high-performance Retrieval-Augmented Generation (RAG) pipelines. \n DevOps & Azure Deployment: Experience with deploying applications on Azure, implementing CI/CD pipelines, and working with Git-based workflows (e.g., Azure DevOps, GitHub Actions). Familiarity with infrastructure automation and release management is a plus.\nIQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com\nIQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements, misrepresentations, or material omissions during the recruitment process will result in immediate disqualification of your application, or termination of employment if discovered later, in accordance with applicable law. 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