Job Description

Country/Region: IN
Requisition ID: 31925
Work Model:
Position Type:
Salary Range:
Location: INDIA - HYDERABAD - BIRLASOFT OFFICE

Title:

Technical Lead-App Development

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Description:

Area(s) of responsibility

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6+ years of experience in Generative AI, focusing on LLMs, NLP techniques, and financial applications.

Key Responsibilities:

Generative AI Model Development: Develop advanced Generative AI models leveraging LLMs (e.g., GPT,Claude,Gemini,LLama) to automate and enhance decision-making, report generation, and analysis, specifically within financial contexts. GenAI Ops: Implement GenAI Ops (Generative AI Operations) principles, managing the AI lifecycle from data operations and model monitoring to maintenance and optimization. Ensure operational readiness and reliability of AI solutions. Human-in-the-Loop (HITL): Establish HITL feedback mechanisms to refine and validate AI-generated outputs. Collaborate with financial domain experts to improve model performance and ensure model accuracy, relevance, and alignment with business objectives. Retrieval-Augmented Generation (RAG): Integrate RAG techniques to enhance LLM performance by enabling the retrieval of up-to-date, authoritative information from external knowledge sources. This is critical for providing accurate and reliable insights, especially in financial applications. Deployment & Scalability: Lead the deployment of GenAI models in cloud environments, ensuring scalability, security, and seamless integration with existing financial systems.




Experience:


Proficiency in GenAI frameworks like LangChain, LlamaIndex, Hugging Face, etc. Strong understanding of Generative AI deployment strategies, including pilot programs, technical assessments, and governance planning. Expertise in GenAI Ops: managing the lifecycle of Generative AI models, including model deployment, monitoring, versioning, and optimization. Hands-on experience in Retrieval-Augmented Generation (RAG) to connect generative models to external data sources for improved performance and accuracy. Understanding of financial datasets and use cases, including financial reporting, risk management, and fraud detection. Proficiency in Python, with deep knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Familiarity with cloud-based platforms like AWS, Azure, or Google Cloud for AI model deployment. Knowledge of MLOps,GenAIOps practices, including version control, experiment tracking, and model monitoring. Strong communication skills, with the ability to explain complex AI concepts to non-technical stakeholders. * Analytical mindset with a focus on innovation and solving complex financial problems using AI.