Birlasoft Limited
Sr Application Developer

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Long Description
Key Responsibilities:
- Application Development: Build GenAI applications from scratch using frameworks like Autogen (applied or acquired), Crew.ai, LangGraph, LlamaIndex, and LangChain.
- Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions.
- Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.
- Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
- Fine-tune SLM (Small Language Model): For domain specific data and use cases.
- Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends.
- Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications.
- Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases.
- LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring.
- Responsible AI Practices: Ensure ethical AI practices are embedded in the development process.
- Innovation:
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Required Skills:
- Python Programming: Deep expertise in Python for building GenAI applications and automation tools.
- Productionization of GenAI application beyond PoCs: Using scale frameworks and tools such as Pylint, Pyrit etc.
- LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
- Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.
- Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
- Fine-tune SLM (Small Language Model): For domain specific data and use cases.
- Prompt Injection Fallback and RCE Tools: Such as Pyrit and HAX toolkit etc.
- Anti-hallucination and Anti-Gibberish Tools: Such as Bleu etc.
- Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development.
- Cloud Platforms: Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. (any two cloud exp.)
- Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. (any one is fine)
- LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management.
- Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations.
- RAG and Modular RAG: Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures.
- Data Modernization: Expertise in modernizing and transforming data for GenAI applications.
- OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud-based tools.
- API Integration: Experience with REST, SOAP, and other protocols for API integration.
- Data Curation: Expertise in building automated data curation and preprocessing pipelines.
- Technical Documentation: Ability to create clear and comprehensive technical documentation.
- Collaboration and Communication: Strong collaboration and communication skills to work effectively with cross-functional teams.


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