Ananya B
Professional Summary
Python Developer with 5+ years of experience building production-grade backend services, REST APIs, and data pipelines across financial services, healthcare, and media analytics. Designs and ships FastAPI and Django REST Framework services with structured request validation, role-based access control, and audit-friendly logging for regulated environments that handle sensitive financial and clinical data. Delivers AI-enabled backend features including LLM-powered document summarization endpoints and retrieval-augmented generation pipelines built with LangChain and FAISS. Manages application data and reporting layers through MySQL, SQLAlchemy, and Django ORM, writing stored procedures and query optimizations that keep downstream reports accurate. Operates Apache Spark ETL jobs on AWS infrastructure and automates operational tasks with Linux shell scripting. Validates services end-to-end with pytest and Postman and applies secure coding standards throughout the development lifecycle.
Technical Skills
Work Experience
- Architected FastAPI services exposing financial records to internal banking applications, owning the full feature lifecycle from requirements gathering through production release.
- Defined Pydantic request and response models with a standardized error-code schema, giving every consuming team consistent, predictable API contracts across services.
- Secured endpoints with token-based authentication and role-based authorization, restricting access to sensitive financial data strictly by user role and permission scope.
- Built a FastAPI service that calls an LLM to summarize and classify internal operations documents, returning structured JSON consumed by downstream banking applications.
- Implemented a retrieval-augmented generation pipeline using LangChain and a FAISS vector index so staff can query internal procedure documents and receive answers grounded in source content.
- Masked account numbers and personal identifiers before transmitting text to the model and validated LLM output against response schemas to maintain compliance and accuracy.
- Constructed pytest suites with fixtures and mocked external dependencies so integration paths run reliably in CI without requiring live upstream banking systems.
- Instrumented services with structured, audit-friendly logging that attaches request IDs, enabling engineers to trace a single transaction across service boundaries during incident investigations.
- Developed Django and Django REST Framework API features for healthcare operations workflows, delivering complete slices from data model design through endpoint implementation and test coverage.
- Modeled relational healthcare data with Django ORM and SQLAlchemy, building the data-access layer that application features relied on for reads and writes.
- Implemented input validation rules that rejected incomplete or inconsistent records submitted by partner healthcare systems before they entered downstream processing.
- Exposed a scikit-learn demand-forecasting model through a Flask REST endpoint, enabling operations applications to request real-time inventory predictions on demand.
- Prepared and cleaned training datasets using pandas, collaborating with data scientists to convert exploratory notebook code into production-grade Python modules.
- Applied HIPAA-aligned data-handling procedures that kept patient identifiers out of application logs and test datasets, escalating compliance risks to the team when identified.
- Reproduced service-to-service integration failures locally using captured request payloads, isolating root causes to roughly 15 minutes per incident on average versus prior ad hoc debugging.
- Maintained Postman collections covering key healthcare service endpoints, enabling QA to reuse them for structured regression testing each sprint without manual rework.
- Developed Apache Spark ETL jobs in Python on AWS EC2 to ingest, clean, and transform media audience data stored in S3, feeding aggregated outputs to downstream client reporting systems.
- Authored MySQL stored procedures that produced the aggregated analytics datasets behind client-facing audience measurement reports delivered to major U.S. media companies.
- Optimized slow reporting queries by rewriting multi-table joins and introducing targeted indexes after analyzing query execution plans, reducing query runtime for the heaviest reports.
- Added data-quality checkpoints — row counts, null checks, and duplicate detection — to pipeline outputs so bad loads were caught before reaching the client reporting layer.
- Built REST endpoints returning JSON and XML payloads to media reporting tools and handled authenticated outbound calls to external data provider services.
- Automated log cleanup, job status checks, and file transfer routines with Unix shell scripts, eliminating a set of repetitive manual operations performed each morning by the support team.
- Provided overnight production support for U.S. media clients across time zones, monitoring scheduled Spark jobs, triaging failures, and rerunning failed data loads to meet SLA windows.
- Submitted all changes through GitLab merge requests and CI pipelines, coordinating deployment handoffs with the onsite U.S. team to ensure smooth release cycles.
- Completed TCS structured training covering SQL, C#, and core software engineering principles, building the foundation required to transition into the Python developer role.
- Built C# console applications applying object-oriented design concepts including classes, inheritance, and encapsulation to solve progressively complex programming exercises.
- Solved assessed data structures and algorithms problems covering arrays, linked lists, sorting, and searching as part of the formal TCS curriculum.
- Practiced SQL query writing — joins, grouping, filtering, and aggregation — on sample databases, validating outputs against expected results to build query accuracy.
- Demonstrated readiness to join production work by completing all assessed modules within the training cohort timeline and earning placement on the Nielsen account team.
- Applied debugging and code-review habits introduced during training to identify and correct logic errors in C# and SQL exercises before final submission.
Projects
- Designed and implemented an energy-efficient cross-layer communication protocol for wireless sensor networks in Python, targeting reduction of redundant transmissions across network layers.
- Conducted code analysis and performance evaluation to benchmark protocol efficiency against baseline implementations, measuring energy consumption and packet delivery outcomes.
- Wrote unit tests covering protocol logic to verify correctness of routing decisions and layer interaction under simulated network conditions.
- Built an Arduino-based autonomous robot capable of following a predefined path using infrared sensor input and real-time control algorithm adjustments.
- Designed the sensor integration circuit and tuned control parameters to maintain stable path-following behavior across varying surface contrast conditions.
Education
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